melb_rain |> model ( logistic = LOGISTIC ( Wet ~ fourier ( K = 5 , period = "year" ) ) ) |> tidy ( ) #> Warning: 1 error encountered for logistic#> [1] there is no package called ‘fable’#> # A tibble: 0 × 3 #> # ℹ 3 variables: .model <chr>, term <chr>, estimate <dbl> #> # A tibble: 11 × 6 #> .model term estimate std.error statistic p.value#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 logistic " (Intercept)" -0.573 0.0321 -17.8 3.18e -71 #> 2 logistic " fourier(K = 5, period = \"ye… -0.325 0.0458 -7.10 1.26e -12 #> 3 logistic " fourier(K = 5, period = \"ye… -0.279 0.0451 -6.18 6.25e -10 #> 4 logistic " fourier(K = 5, period = \"ye… -0.020 3 0.0455 -0.446 6.56e - 1 #> 5 logistic " fourier(K = 5, period = \"ye… -0.031 2 0.0453 -0.688 4.91e - 1 #> 6 logistic " fourier(K = 5, period = \"ye… -0.069 6 0.0454 -1.53 1.26e - 1 #> 7 logistic " fourier(K = 5, period = \"ye… -0.020 7 0.0454 -0.457 6.48e - 1 #> 8 logistic " fourier(K = 5, period = \"ye… -0.034 2 0.0454 -0.754 4.51e - 1 #> 9 logistic " fourier(K = 5, period = \"ye… 0.0224 0.0454 0.494 6.21e - 1 #> 10 logistic " fourier(K = 5, period = \"ye… -0.018 8 0.0453 -0.415 6.78e - 1 #> 11 logistic " fourier(K = 5, period = \"ye… 0.00815 0.0453 0.180 8.57e - 1 On this page
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-[{"path":"https://pkg.robjhyndman.com/fable.binary/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Rob Hyndman. Author, maintainer, copyright holder. Mitchell O'Hara-Wild. Author.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Hyndman R, O'Hara-Wild M (2023). fable.binary: Forecasting Binary Time Series. R package version 0.1.0, https://github.com/robjhyndman/fable.binary.","code":"@Manual{, title = {fable.binary: Forecasting Binary Time Series}, author = {Rob Hyndman and Mitchell O'Hara-Wild}, year = {2023}, note = {R package version 0.1.0}, url = {https://github.com/robjhyndman/fable.binary}, }"},{"path":"https://pkg.robjhyndman.com/fable.binary/index.html","id":"fablebinary","dir":"","previous_headings":"","what":"Forecasting Binary Time Series","title":"Forecasting Binary Time Series","text":"R package fable.binary provides collection time series forecasting models suitable binary time series. models work within fable framework, provides tools evaluate, visualise, combine models workflow consistent tidyverse.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Forecasting Binary Time Series","text":"can install development version GitHub","code":"# install.packages(\"remotes\") remotes::install_github(\"robjhyndman/fable.binary\")"},{"path":"https://pkg.robjhyndman.com/fable.binary/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Forecasting Binary Time Series","text":"","code":"library(fable.binary) #> Loading required package: fabletools library(ggplot2) library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union # Fit models fit <- melb_rain |> model( nn = BINNET(Wet ~ fourier(K = 1, period = \"year\")), logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\")) ) # Functions for computing on models fit |> tidy() #> # A tibble: 11 × 6 #> .model term estimate std.error statistic p.value #> #> 1 logistic \"(Intercept)\" -0.573 0.0321 -17.8 3.18e-71 #> 2 logistic \"fourier(K = 5, period = \\\"ye… -0.325 0.0458 -7.10 1.26e-12 #> 3 logistic \"fourier(K = 5, period = \\\"ye… -0.279 0.0451 -6.18 6.25e-10 #> 4 logistic \"fourier(K = 5, period = \\\"ye… -0.0203 0.0455 -0.446 6.56e- 1 #> 5 logistic \"fourier(K = 5, period = \\\"ye… -0.0312 0.0453 -0.688 4.91e- 1 #> 6 logistic \"fourier(K = 5, period = \\\"ye… -0.0696 0.0454 -1.53 1.26e- 1 #> 7 logistic \"fourier(K = 5, period = \\\"ye… -0.0207 0.0454 -0.457 6.48e- 1 #> 8 logistic \"fourier(K = 5, period = \\\"ye… -0.0342 0.0454 -0.754 4.51e- 1 #> 9 logistic \"fourier(K = 5, period = \\\"ye… 0.0224 0.0454 0.494 6.21e- 1 #> 10 logistic \"fourier(K = 5, period = \\\"ye… -0.0188 0.0453 -0.415 6.78e- 1 #> 11 logistic \"fourier(K = 5, period = \\\"ye… 0.00815 0.0453 0.180 8.57e- 1 fit |> select(logistic) |> glance() #> # A tibble: 1 × 12 #> .model df log_lik AIC AICc BIC deviance df.residual rank #> #> 1 logistic 11 -2787. 5596. 5596. 5666. 5574. 4311 11 #> # ℹ 3 more variables: null_deviance , df_null , nobs fit |> select(logistic) |> report() #> Series: Wet #> Model: LOGISTIC #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) -0.573059 0.032114 -17.845 < 2e-16 *** #> fourier(K = 5, period = \"year\")C1_365 -0.324774 0.045754 -7.098 1.26e-12 *** #> fourier(K = 5, period = \"year\")S1_365 -0.278737 0.045075 -6.184 6.25e-10 *** #> fourier(K = 5, period = \"year\")C2_365 -0.020283 0.045519 -0.446 0.656 #> fourier(K = 5, period = \"year\")S2_365 -0.031195 0.045310 -0.688 0.491 #> fourier(K = 5, period = \"year\")C3_365 -0.069556 0.045449 -1.530 0.126 #> fourier(K = 5, period = \"year\")S3_365 -0.020730 0.045374 -0.457 0.648 #> fourier(K = 5, period = \"year\")C4_365 -0.034241 0.045438 -0.754 0.451 #> fourier(K = 5, period = \"year\")S4_365 0.022435 0.045375 0.494 0.621 #> fourier(K = 5, period = \"year\")C5_365 -0.018772 0.045275 -0.415 0.678 #> fourier(K = 5, period = \"year\")S5_365 0.008153 0.045337 0.180 0.857 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> # A tibble: 1 × 11 #> df log_lik AIC AICc BIC deviance df.residual rank null_deviance #> #> 1 11 -2787. 5596. 5596. 5666. 5574. 4311 11 5668. #> # ℹ 2 more variables: df_null , nobs fit |> select(nn) |> glance() #> # A tibble: 1 × 6 #> .model inputs hidden_nodes weights repeats sigma2 #> #> 1 nn 2 2 9 20 0.227 fit |> select(nn) |> report() #> Series: Wet #> Model: BINNET: 2 #> #> Average of 20 networks, each of which is #> a 2-2-1 network with 9 weights #> options were - #> #> sigma^2 estimated as 0.2269 augment(fit) #> # A tsibble: 8,644 x 6 [1D] #> # Key: .model [2] #> .model Date Wet .fitted .resid .innov #> #> 1 nn 2000-01-01 TRUE 0.255 0.745 0.745 #> 2 nn 2000-01-02 FALSE 0.254 -0.254 -0.254 #> 3 nn 2000-01-03 FALSE 0.253 -0.253 -0.253 #> 4 nn 2000-01-04 TRUE 0.252 0.748 0.748 #> 5 nn 2000-01-05 TRUE 0.251 0.749 0.749 #> 6 nn 2000-01-06 FALSE 0.250 -0.250 -0.250 #> 7 nn 2000-01-07 FALSE 0.249 -0.249 -0.249 #> 8 nn 2000-01-08 FALSE 0.248 -0.248 -0.248 #> 9 nn 2000-01-09 FALSE 0.248 -0.248 -0.248 #> 10 nn 2000-01-10 TRUE 0.247 0.753 0.753 #> # ℹ 8,634 more rows # Produce forecasts. For neural network, use fc <- forecast(fit, h = \"2 years\") as_tibble(fc) |> ggplot(aes(x = Date, y = .mean, col = .model)) + geom_line() + labs(y = \"Probability of rain\")"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Neural Network Binary Time Series Forecasts — BINNET","title":"Neural Network Binary Time Series Forecasts — BINNET","text":"Feed-forward neural networks single hidden layer lagged inputs forecasting univariate binary time series.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Neural Network Binary Time Series Forecasts — BINNET","text":"","code":"BINNET(formula, n_nodes = NULL, n_networks = 20, scale_inputs = TRUE, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Neural Network Binary Time Series Forecasts — BINNET","text":"formula Model specification (see \"Specials\" section). n_nodes Number nodes hidden layer. Default half number external regressors plus 1. n_networks Number networks fit different random starting weights. averaged producing forecasts. scale_inputs TRUE, inputs scaled subtracting column means dividing respective standard deviations. ... arguments passed \\link[nnet]{nnet}.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/BINNET.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Neural Network Binary Time Series Forecasts — BINNET","text":"model specification.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/BINNET.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Neural Network Binary Time Series Forecasts — BINNET","text":"feed-forward neural network fitted single hidden layer containing size nodes. Exogenous regressors used inputs. total repeats networks fitted, random starting weights. averaged computing forecasts.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Neural Network Binary Time Series Forecasts — BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) #> Warning: 1 error encountered for nn #> [1] there is no package called ‘fable’ #> # A mable: 1 x 1 #> nn #> #> 1 "},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a linear model with time series components — LOGISTIC","title":"Fit a linear model with time series components — LOGISTIC","text":"model formula handled using stats::model.matrix(), approach include interactions stats::lm() applies specifying formula. addition stats::lm(), possible include common_xregs model formula, trend(), season(), fourier().","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a linear model with time series components — LOGISTIC","text":"","code":"LOGISTIC(formula)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a linear model with time series components — LOGISTIC","text":"formula Model specification.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/LOGISTIC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a linear model with time series components — LOGISTIC","text":"model specification.","code":""},{"path":[]},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/LOGISTIC.html","id":"xreg","dir":"Reference","previous_headings":"","what":"xreg","title":"Fit a linear model with time series components — LOGISTIC","text":"Exogenous regressors can included LOGISTIC model without explicitly using xreg() special. Common exogenous regressor specials specified common_xregs can also used. regressors handled using stats::model.frame(), interactions functionality behaves similarly stats::lm().","code":"xreg(...)"},{"path":[]},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a linear model with time series components — LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) #> Warning: 1 error encountered for logistic #> [1] there is no package called ‘fable’ #> # A mable: 1 x 1 #> logistic #> #> 1 "},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/common_xregs.html","id":null,"dir":"Reference","previous_headings":"","what":"Common exogenous regressors — common_xregs","title":"Common exogenous regressors — common_xregs","text":"special functions provide interfaces complicated functions within model formulae interface.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/common_xregs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Common exogenous regressors — common_xregs","text":"","code":"common_xregs"},{"path":[]},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/common_xregs.html","id":"trend","dir":"Reference","previous_headings":"","what":"trend","title":"Common exogenous regressors — common_xregs","text":"trend special includes common linear trend regressors model. also supports piecewise linear trend via knots argument.","code":"trend(knots = NULL, origin = NULL)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/common_xregs.html","id":"season","dir":"Reference","previous_headings":"","what":"season","title":"Common exogenous regressors — common_xregs","text":"season special includes seasonal dummy variables model.","code":"season(period = NULL)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/common_xregs.html","id":"fourier","dir":"Reference","previous_headings":"","what":"fourier","title":"Common exogenous regressors — common_xregs","text":"fourier special includes seasonal fourier terms model. maximum order fourier terms must specified using K.","code":"fourier(period = NULL, K, origin = NULL)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fable.binary-package.html","id":null,"dir":"Reference","previous_headings":"","what":"fable.binary: Forecasting Binary Time Series — fable.binary-package","title":"fable.binary: Forecasting Binary Time Series — fable.binary-package","text":"Provides collection time series forecasting models suitable binary time series. models work within 'fable' framework provided 'fabletools' package, provides tools evaluate, visualise, combine models workflow consistent tidyverse.","code":""},{"path":[]},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fable.binary-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"fable.binary: Forecasting Binary Time Series — fable.binary-package","text":"Maintainer: Rob Hyndman Rob.Hyndman@monash.edu [copyright holder] Authors: Mitchell O'Hara-Wild mail@mitchelloharawild.com","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract fitted values from a fable model — fitted.BINNET","title":"Extract fitted values from a fable model — fitted.BINNET","text":"Extracts fitted values.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract fitted values from a fable model — fitted.BINNET","text":"","code":"# S3 method for BINNET fitted(object, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract fitted values from a fable model — fitted.BINNET","text":"object Fitted model ... arguments ignored","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract fitted values from a fable model — fitted.BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) |> fitted() #> Warning: 1 error encountered for nn #> [1] there is no package called ‘fable’ #> # A tsibble: 4,322 x 3 [1D] #> # Key: .model [1] #> .model Date .fitted #> #> 1 nn 2000-01-01 NA #> 2 nn 2000-01-02 NA #> 3 nn 2000-01-03 NA #> 4 nn 2000-01-04 NA #> 5 nn 2000-01-05 NA #> 6 nn 2000-01-06 NA #> 7 nn 2000-01-07 NA #> 8 nn 2000-01-08 NA #> 9 nn 2000-01-09 NA #> 10 nn 2000-01-10 NA #> # ℹ 4,312 more rows"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract fitted values from a fable model — fitted.LOGISTIC","title":"Extract fitted values from a fable model — fitted.LOGISTIC","text":"Extracts fitted values.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract fitted values from a fable model — fitted.LOGISTIC","text":"","code":"# S3 method for LOGISTIC fitted(object, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract fitted values from a fable model — fitted.LOGISTIC","text":"object Fitted model ... arguments ignored","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract fitted values from a fable model — fitted.LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) |> fitted() #> Warning: 1 error encountered for logistic #> [1] there is no package called ‘fable’ #> # A tsibble: 4,322 x 3 [1D] #> # Key: .model [1] #> .model Date .fitted #> #> 1 logistic 2000-01-01 NA #> 2 logistic 2000-01-02 NA #> 3 logistic 2000-01-03 NA #> 4 logistic 2000-01-04 NA #> 5 logistic 2000-01-05 NA #> 6 logistic 2000-01-06 NA #> 7 logistic 2000-01-07 NA #> 8 logistic 2000-01-08 NA #> 9 logistic 2000-01-09 NA #> 10 logistic 2000-01-10 NA #> # ℹ 4,312 more rows"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Forecast a model from the fable package — forecast.BINNET","title":"Forecast a model from the fable package — forecast.BINNET","text":"Produces forecasts trained model.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forecast a model from the fable package — forecast.BINNET","text":"","code":"# S3 method for BINNET forecast(object, new_data, specials = NULL, simulate = TRUE, times = 5000, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forecast a model from the fable package — forecast.BINNET","text":"object model forecasts required. new_data tsibble containing time points exogenous regressors produce forecasts . specials (passed fabletools::forecast.mdl_df()). simulate TRUE, forecast distributions computed using simulation Bernoulli model. times number sample paths use estimating forecast distribution simulate = TRUE. ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forecast a model from the fable package — forecast.BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) |> forecast(times = 10) #> Warning: 1 error encountered for nn #> [1] there is no package called ‘fable’ #> # A fable: 14 x 4 [1D] #> # Key: .model [1] #> .model Date Wet .mean #> #> 1 nn 2011-11-01 NA NA #> 2 nn 2011-11-02 NA NA #> 3 nn 2011-11-03 NA NA #> 4 nn 2011-11-04 NA NA #> 5 nn 2011-11-05 NA NA #> 6 nn 2011-11-06 NA NA #> 7 nn 2011-11-07 NA NA #> 8 nn 2011-11-08 NA NA #> 9 nn 2011-11-09 NA NA #> 10 nn 2011-11-10 NA NA #> 11 nn 2011-11-11 NA NA #> 12 nn 2011-11-12 NA NA #> 13 nn 2011-11-13 NA NA #> 14 nn 2011-11-14 NA NA"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Forecast a model from the fable package — forecast.LOGISTIC","title":"Forecast a model from the fable package — forecast.LOGISTIC","text":"Produces forecasts trained model.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forecast a model from the fable package — forecast.LOGISTIC","text":"","code":"# S3 method for LOGISTIC forecast( object, new_data, specials = NULL, simulate = FALSE, times = 5000, ... )"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forecast a model from the fable package — forecast.LOGISTIC","text":"object model forecasts required. new_data tsibble containing time points exogenous regressors produce forecasts . specials (passed fabletools::forecast.mdl_df()). simulate TRUE, forecast distributions computed using simulation Bernoulli model. times number sample paths use estimating forecast distribution simulate = TRUE. ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forecast a model from the fable package — forecast.LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) |> forecast(h = \"2 years\") #> Warning: 1 error encountered for logistic #> [1] there is no package called ‘fable’ #> # A fable: 730 x 4 [1D] #> # Key: .model [1] #> .model Date Wet .mean #> #> 1 logistic 2011-11-01 NA NA #> 2 logistic 2011-11-02 NA NA #> 3 logistic 2011-11-03 NA NA #> 4 logistic 2011-11-04 NA NA #> 5 logistic 2011-11-05 NA NA #> 6 logistic 2011-11-06 NA NA #> 7 logistic 2011-11-07 NA NA #> 8 logistic 2011-11-08 NA NA #> 9 logistic 2011-11-09 NA NA #> 10 logistic 2011-11-10 NA NA #> # ℹ 720 more rows"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate new data from a fable model — generate.BINNET","title":"Generate new data from a fable model — generate.BINNET","text":"Simulates future paths dataset using fitted model.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate new data from a fable model — generate.BINNET","text":"","code":"# S3 method for BINNET generate(x, new_data, specials = NULL, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate new data from a fable model — generate.BINNET","text":"x object. new_data tsibble containing time points exogenous regressors produce forecasts . specials (passed fabletools::generate.mdl_df()). ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate new data from a fable model — generate.BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) |> generate() #> Warning: 1 error encountered for nn #> [1] there is no package called ‘fable’ #> # A tsibble: 14 x 4 [1D] #> # Key: .model, .rep [1] #> .model .rep Date .sim #> #> 1 nn 1 2011-11-01 NA #> 2 nn 1 2011-11-02 NA #> 3 nn 1 2011-11-03 NA #> 4 nn 1 2011-11-04 NA #> 5 nn 1 2011-11-05 NA #> 6 nn 1 2011-11-06 NA #> 7 nn 1 2011-11-07 NA #> 8 nn 1 2011-11-08 NA #> 9 nn 1 2011-11-09 NA #> 10 nn 1 2011-11-10 NA #> 11 nn 1 2011-11-11 NA #> 12 nn 1 2011-11-12 NA #> 13 nn 1 2011-11-13 NA #> 14 nn 1 2011-11-14 NA"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate new data from a fable model — generate.LOGISTIC","title":"Generate new data from a fable model — generate.LOGISTIC","text":"Simulates future paths dataset using fitted model.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate new data from a fable model — generate.LOGISTIC","text":"","code":"# S3 method for LOGISTIC generate(x, new_data, specials, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate new data from a fable model — generate.LOGISTIC","text":"x object. new_data tsibble containing time points exogenous regressors produce forecasts . specials (passed fabletools::generate.mdl_df()). ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate new data from a fable model — generate.LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) |> generate() #> Warning: 1 error encountered for logistic #> [1] there is no package called ‘fable’ #> # A tsibble: 14 x 4 [1D] #> # Key: .model, .rep [1] #> .model .rep Date .sim #> #> 1 logistic 1 2011-11-01 NA #> 2 logistic 1 2011-11-02 NA #> 3 logistic 1 2011-11-03 NA #> 4 logistic 1 2011-11-04 NA #> 5 logistic 1 2011-11-05 NA #> 6 logistic 1 2011-11-06 NA #> 7 logistic 1 2011-11-07 NA #> 8 logistic 1 2011-11-08 NA #> 9 logistic 1 2011-11-09 NA #> 10 logistic 1 2011-11-10 NA #> 11 logistic 1 2011-11-11 NA #> 12 logistic 1 2011-11-12 NA #> 13 logistic 1 2011-11-13 NA #> 14 logistic 1 2011-11-14 NA"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Glance a BINNET model — glance.BINNET","title":"Glance a BINNET model — glance.BINNET","text":"Construct single row summary BINNET model. Contains variance residuals (sigma2).","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glance a BINNET model — glance.BINNET","text":"","code":"# S3 method for BINNET glance(x, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glance a BINNET model — glance.BINNET","text":"x model R object convert single-row data frame ... arguments ignored","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.BINNET.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Glance a BINNET model — glance.BINNET","text":"one row tibble summarising model's fit.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Glance a BINNET model — glance.BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) |> glance() #> Warning: 1 error encountered for nn #> [1] there is no package called ‘fable’ #> # A tibble: 0 × 1 #> # ℹ 1 variable: .model "},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Glance a LOGISTIC — glance.LOGISTIC","title":"Glance a LOGISTIC — glance.LOGISTIC","text":"Construct single row summary LOGISTIC model.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glance a LOGISTIC — glance.LOGISTIC","text":"","code":"# S3 method for LOGISTIC glance(x, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glance a LOGISTIC — glance.LOGISTIC","text":"x model R object convert single-row data frame ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.LOGISTIC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Glance a LOGISTIC — glance.LOGISTIC","text":"one row tibble summarising model's fit.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.LOGISTIC.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Glance a LOGISTIC — glance.LOGISTIC","text":"Contains R squared (r_squared), variance residuals (sigma2), log-likelihood (log_lik), information criterion (AIC, AICc, BIC).","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Glance a LOGISTIC — glance.LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) |> glance() #> Warning: 1 error encountered for logistic #> [1] there is no package called ‘fable’ #> # A tibble: 0 × 1 #> # ℹ 1 variable: .model "},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/melb_rain.html","id":null,"dir":"Reference","previous_headings":"","what":"Daily rainfall in Melbourne, Australia — melb_rain","title":"Daily rainfall in Melbourne, Australia — melb_rain","text":"dataset containing daily rainfall amounts 1 January 2000 31 December 2011.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/melb_rain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Daily rainfall in Melbourne, Australia — melb_rain","text":"","code":"melb_rain"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/melb_rain.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Daily rainfall in Melbourne, Australia — melb_rain","text":"tsibble data frame 4322 rows 3 variables.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/melb_rain.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Daily rainfall in Melbourne, Australia — melb_rain","text":"Australian Bureau Meteorology. http://www.bom.gov.au/climate/data/","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. dplyr %>% tsibble as_tsibble","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Refit a BINNET model — refit.BINNET","title":"Refit a BINNET model — refit.BINNET","text":"Applies fitted BINNET model new dataset.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Refit a BINNET model — refit.BINNET","text":"","code":"# S3 method for BINNET refit(object, new_data, specials = NULL, reestimate = FALSE, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Refit a BINNET model — refit.BINNET","text":"object model forecasts required. new_data tsibble containing time points exogenous regressors produce forecasts . specials (passed fabletools::forecast.mdl_df()). reestimate TRUE, networks initialized random starting weights suit new data. FALSE, every network best individual set weights found pre-estimation process used starting weight vector. ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.BINNET.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Refit a BINNET model — refit.BINNET","text":"refitted model.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Refit a LOGISTIC — refit.LOGISTIC","title":"Refit a LOGISTIC — refit.LOGISTIC","text":"Applies fitted LOGISTIC new dataset.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Refit a LOGISTIC — refit.LOGISTIC","text":"","code":"# S3 method for LOGISTIC refit(object, new_data, specials = NULL, reestimate = FALSE, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Refit a LOGISTIC — refit.LOGISTIC","text":"object fitted model object. new_data tsibble containing time points exogenous regressors refit required. specials list special functions used model, (passed fabletools::forecast.mdl_df). reestimate TRUE, networks initialized random starting weights suit new data. FALSE, every network best individual set weights found pre-estimation process used starting weight vector. ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract residuals from a fable model. — residuals.BINNET","title":"Extract residuals from a fable model. — residuals.BINNET","text":"Extracts residuals.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract residuals from a fable model. — residuals.BINNET","text":"","code":"# S3 method for BINNET residuals(object, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract residuals from a fable model. — residuals.BINNET","text":"object Fitted model ... arguments ignored","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract residuals from a fable model. — residuals.BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) |> residuals() #> Warning: 1 error encountered for nn #> [1] there is no package called ‘fable’ #> # A tsibble: 4,322 x 3 [1D] #> # Key: .model [1] #> .model Date .resid #> #> 1 nn 2000-01-01 NA #> 2 nn 2000-01-02 NA #> 3 nn 2000-01-03 NA #> 4 nn 2000-01-04 NA #> 5 nn 2000-01-05 NA #> 6 nn 2000-01-06 NA #> 7 nn 2000-01-07 NA #> 8 nn 2000-01-08 NA #> 9 nn 2000-01-09 NA #> 10 nn 2000-01-10 NA #> # ℹ 4,312 more rows"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract residuals from a fable model. — residuals.LOGISTIC","title":"Extract residuals from a fable model. — residuals.LOGISTIC","text":"Extracts residuals.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract residuals from a fable model. — residuals.LOGISTIC","text":"","code":"# S3 method for LOGISTIC residuals( object, type = c(\"deviance\", \"innovation\", \"pearson\", \"working\", \"response\", \"partial\"), ... )"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract residuals from a fable model. — residuals.LOGISTIC","text":"object Fitted model type type residuals returned. alternatives : \"deviance\" (default), \"pearson\", \"working\", \"response\", \"partial\". Can abbreviated. ... arguments ignored","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract residuals from a fable model. — residuals.LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) |> residuals(type = \"deviance\") #> Warning: 1 error encountered for logistic #> [1] there is no package called ‘fable’ #> # A tsibble: 4,322 x 3 [1D] #> # Key: .model [1] #> .model Date .resid #> #> 1 logistic 2000-01-01 NA #> 2 logistic 2000-01-02 NA #> 3 logistic 2000-01-03 NA #> 4 logistic 2000-01-04 NA #> 5 logistic 2000-01-05 NA #> 6 logistic 2000-01-06 NA #> 7 logistic 2000-01-07 NA #> 8 logistic 2000-01-08 NA #> 9 logistic 2000-01-09 NA #> 10 logistic 2000-01-10 NA #> # ℹ 4,312 more rows"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy a fable model — tidy.BINNET","title":"Tidy a fable model — tidy.BINNET","text":"Returns coefficients model tibble format.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy a fable model — tidy.BINNET","text":"","code":"# S3 method for BINNET tidy(x, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy a fable model — tidy.BINNET","text":"x object converted tidy tibble::tibble(). ... Additional arguments tidying method.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy a fable model — tidy.BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) |> tidy() #> Warning: 1 error encountered for nn #> [1] there is no package called ‘fable’ #> # A tibble: 0 × 3 #> # ℹ 3 variables: .model , term , estimate "},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy a fable model — tidy.LOGISTIC","title":"Tidy a fable model — tidy.LOGISTIC","text":"Returns coefficients model tibble format.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy a fable model — tidy.LOGISTIC","text":"","code":"# S3 method for LOGISTIC tidy(x, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy a fable model — tidy.LOGISTIC","text":"x object converted tidy tibble::tibble(). ... Additional arguments tidying method.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy a fable model — tidy.LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) |> tidy() #> Warning: 1 error encountered for logistic #> [1] there is no package called ‘fable’ #> # A tibble: 0 × 3 #> # ℹ 3 variables: .model , term , estimate "},{"path":"https://pkg.robjhyndman.com/fable.binary/news/index.html","id":"fablebinary-development-version","dir":"Changelog","previous_headings":"","what":"fable.binary (development version)","title":"fable.binary (development version)","text":"Added LOGISTIC BINNET models","code":""}]
+[{"path":"https://pkg.robjhyndman.com/fable.binary/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Rob Hyndman. Author, maintainer, copyright holder. Mitchell O'Hara-Wild. Author.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Hyndman R, O'Hara-Wild M (2023). fable.binary: Forecasting Binary Time Series. R package version 0.1.0, https://github.com/robjhyndman/fable.binary.","code":"@Manual{, title = {fable.binary: Forecasting Binary Time Series}, author = {Rob Hyndman and Mitchell O'Hara-Wild}, year = {2023}, note = {R package version 0.1.0}, url = {https://github.com/robjhyndman/fable.binary}, }"},{"path":"https://pkg.robjhyndman.com/fable.binary/index.html","id":"fablebinary","dir":"","previous_headings":"","what":"Forecasting Binary Time Series","title":"Forecasting Binary Time Series","text":"R package fable.binary provides collection time series forecasting models suitable binary time series. models work within fable framework, provides tools evaluate, visualise, combine models workflow consistent tidyverse.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Forecasting Binary Time Series","text":"can install development version GitHub","code":"# install.packages(\"remotes\") remotes::install_github(\"robjhyndman/fable.binary\")"},{"path":"https://pkg.robjhyndman.com/fable.binary/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Forecasting Binary Time Series","text":"","code":"library(fable.binary) #> Loading required package: fabletools library(ggplot2) library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union # Fit models fit <- melb_rain |> model( nn = BINNET(Wet ~ fourier(K = 1, period = \"year\")), logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\")) ) # Functions for computing on models fit |> tidy() #> # A tibble: 11 × 6 #> .model term estimate std.error statistic p.value #> #> 1 logistic \"(Intercept)\" -0.573 0.0321 -17.8 3.18e-71 #> 2 logistic \"fourier(K = 5, period = \\\"ye… -0.325 0.0458 -7.10 1.26e-12 #> 3 logistic \"fourier(K = 5, period = \\\"ye… -0.279 0.0451 -6.18 6.25e-10 #> 4 logistic \"fourier(K = 5, period = \\\"ye… -0.0203 0.0455 -0.446 6.56e- 1 #> 5 logistic \"fourier(K = 5, period = \\\"ye… -0.0312 0.0453 -0.688 4.91e- 1 #> 6 logistic \"fourier(K = 5, period = \\\"ye… -0.0696 0.0454 -1.53 1.26e- 1 #> 7 logistic \"fourier(K = 5, period = \\\"ye… -0.0207 0.0454 -0.457 6.48e- 1 #> 8 logistic \"fourier(K = 5, period = \\\"ye… -0.0342 0.0454 -0.754 4.51e- 1 #> 9 logistic \"fourier(K = 5, period = \\\"ye… 0.0224 0.0454 0.494 6.21e- 1 #> 10 logistic \"fourier(K = 5, period = \\\"ye… -0.0188 0.0453 -0.415 6.78e- 1 #> 11 logistic \"fourier(K = 5, period = \\\"ye… 0.00815 0.0453 0.180 8.57e- 1 fit |> select(logistic) |> glance() #> # A tibble: 1 × 12 #> .model df log_lik AIC AICc BIC deviance df.residual rank #> #> 1 logistic 11 -2787. 5596. 5596. 5666. 5574. 4311 11 #> # ℹ 3 more variables: null_deviance , df_null , nobs fit |> select(logistic) |> report() #> Series: Wet #> Model: LOGISTIC #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) -0.573059 0.032114 -17.845 < 2e-16 *** #> fourier(K = 5, period = \"year\")C1_365 -0.324774 0.045754 -7.098 1.26e-12 *** #> fourier(K = 5, period = \"year\")S1_365 -0.278737 0.045075 -6.184 6.25e-10 *** #> fourier(K = 5, period = \"year\")C2_365 -0.020283 0.045519 -0.446 0.656 #> fourier(K = 5, period = \"year\")S2_365 -0.031195 0.045310 -0.688 0.491 #> fourier(K = 5, period = \"year\")C3_365 -0.069556 0.045449 -1.530 0.126 #> fourier(K = 5, period = \"year\")S3_365 -0.020730 0.045374 -0.457 0.648 #> fourier(K = 5, period = \"year\")C4_365 -0.034241 0.045438 -0.754 0.451 #> fourier(K = 5, period = \"year\")S4_365 0.022435 0.045375 0.494 0.621 #> fourier(K = 5, period = \"year\")C5_365 -0.018772 0.045275 -0.415 0.678 #> fourier(K = 5, period = \"year\")S5_365 0.008153 0.045337 0.180 0.857 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> # A tibble: 1 × 11 #> df log_lik AIC AICc BIC deviance df.residual rank null_deviance #> #> 1 11 -2787. 5596. 5596. 5666. 5574. 4311 11 5668. #> # ℹ 2 more variables: df_null , nobs fit |> select(nn) |> glance() #> # A tibble: 1 × 6 #> .model inputs hidden_nodes weights repeats sigma2 #> #> 1 nn 2 2 9 20 0.227 fit |> select(nn) |> report() #> Series: Wet #> Model: BINNET: 2 #> #> Average of 20 networks, each of which is #> a 2-2-1 network with 9 weights #> options were - #> #> sigma^2 estimated as 0.2269 augment(fit) #> # A tsibble: 8,644 x 6 [1D] #> # Key: .model [2] #> .model Date Wet .fitted .resid .innov #> #> 1 nn 2000-01-01 TRUE 0.255 0.745 0.745 #> 2 nn 2000-01-02 FALSE 0.254 -0.254 -0.254 #> 3 nn 2000-01-03 FALSE 0.253 -0.253 -0.253 #> 4 nn 2000-01-04 TRUE 0.252 0.748 0.748 #> 5 nn 2000-01-05 TRUE 0.251 0.749 0.749 #> 6 nn 2000-01-06 FALSE 0.250 -0.250 -0.250 #> 7 nn 2000-01-07 FALSE 0.249 -0.249 -0.249 #> 8 nn 2000-01-08 FALSE 0.248 -0.248 -0.248 #> 9 nn 2000-01-09 FALSE 0.248 -0.248 -0.248 #> 10 nn 2000-01-10 TRUE 0.247 0.753 0.753 #> # ℹ 8,634 more rows # Produce forecasts. For neural network, use fc <- forecast(fit, h = \"2 years\") as_tibble(fc) |> ggplot(aes(x = Date, y = .mean, col = .model)) + geom_line() + labs(y = \"Probability of rain\")"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Neural Network Binary Time Series Forecasts — BINNET","title":"Neural Network Binary Time Series Forecasts — BINNET","text":"Feed-forward neural networks single hidden layer lagged inputs forecasting univariate binary time series.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Neural Network Binary Time Series Forecasts — BINNET","text":"","code":"BINNET(formula, n_nodes = NULL, n_networks = 20, scale_inputs = TRUE, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Neural Network Binary Time Series Forecasts — BINNET","text":"formula Model specification (see \"Specials\" section). n_nodes Number nodes hidden layer. Default half number external regressors plus 1. n_networks Number networks fit different random starting weights. averaged producing forecasts. scale_inputs TRUE, inputs scaled subtracting column means dividing respective standard deviations. ... arguments passed \\link[nnet]{nnet}.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/BINNET.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Neural Network Binary Time Series Forecasts — BINNET","text":"model specification.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/BINNET.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Neural Network Binary Time Series Forecasts — BINNET","text":"feed-forward neural network fitted single hidden layer containing size nodes. Exogenous regressors used inputs. total repeats networks fitted, random starting weights. averaged computing forecasts.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Neural Network Binary Time Series Forecasts — BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) #> # A mable: 1 x 1 #> nn #> #> 1 "},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a linear model with time series components — LOGISTIC","title":"Fit a linear model with time series components — LOGISTIC","text":"model formula handled using stats::model.matrix(), approach include interactions stats::lm() applies specifying formula. addition stats::lm(), possible include common_xregs model formula, trend(), season(), fourier().","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a linear model with time series components — LOGISTIC","text":"","code":"LOGISTIC(formula)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a linear model with time series components — LOGISTIC","text":"formula Model specification.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/LOGISTIC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a linear model with time series components — LOGISTIC","text":"model specification.","code":""},{"path":[]},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/LOGISTIC.html","id":"xreg","dir":"Reference","previous_headings":"","what":"xreg","title":"Fit a linear model with time series components — LOGISTIC","text":"Exogenous regressors can included LOGISTIC model without explicitly using xreg() special. Common exogenous regressor specials specified common_xregs can also used. regressors handled using stats::model.frame(), interactions functionality behaves similarly stats::lm().","code":"xreg(...)"},{"path":[]},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Fit a linear model with time series components — LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) #> # A mable: 1 x 1 #> logistic #> #> 1 "},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/common_xregs.html","id":null,"dir":"Reference","previous_headings":"","what":"Common exogenous regressors — common_xregs","title":"Common exogenous regressors — common_xregs","text":"special functions provide interfaces complicated functions within model formulae interface.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/common_xregs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Common exogenous regressors — common_xregs","text":"","code":"common_xregs"},{"path":[]},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/common_xregs.html","id":"trend","dir":"Reference","previous_headings":"","what":"trend","title":"Common exogenous regressors — common_xregs","text":"trend special includes common linear trend regressors model. also supports piecewise linear trend via knots argument.","code":"trend(knots = NULL, origin = NULL)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/common_xregs.html","id":"season","dir":"Reference","previous_headings":"","what":"season","title":"Common exogenous regressors — common_xregs","text":"season special includes seasonal dummy variables model.","code":"season(period = NULL)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/common_xregs.html","id":"fourier","dir":"Reference","previous_headings":"","what":"fourier","title":"Common exogenous regressors — common_xregs","text":"fourier special includes seasonal fourier terms model. maximum order fourier terms must specified using K.","code":"fourier(period = NULL, K, origin = NULL)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fable.binary-package.html","id":null,"dir":"Reference","previous_headings":"","what":"fable.binary: Forecasting Binary Time Series — fable.binary-package","title":"fable.binary: Forecasting Binary Time Series — fable.binary-package","text":"Provides collection time series forecasting models suitable binary time series. models work within 'fable' framework provided 'fabletools' package, provides tools evaluate, visualise, combine models workflow consistent tidyverse.","code":""},{"path":[]},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fable.binary-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"fable.binary: Forecasting Binary Time Series — fable.binary-package","text":"Maintainer: Rob Hyndman Rob.Hyndman@monash.edu [copyright holder] Authors: Mitchell O'Hara-Wild mail@mitchelloharawild.com","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract fitted values from a fable model — fitted.BINNET","title":"Extract fitted values from a fable model — fitted.BINNET","text":"Extracts fitted values.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract fitted values from a fable model — fitted.BINNET","text":"","code":"# S3 method for BINNET fitted(object, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract fitted values from a fable model — fitted.BINNET","text":"object Fitted model ... arguments ignored","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract fitted values from a fable model — fitted.BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) |> fitted() #> # A tsibble: 4,322 x 3 [1D] #> # Key: .model [1] #> .model Date .fitted #> #> 1 nn 2000-01-01 0.268 #> 2 nn 2000-01-02 0.267 #> 3 nn 2000-01-03 0.266 #> 4 nn 2000-01-04 0.265 #> 5 nn 2000-01-05 0.264 #> 6 nn 2000-01-06 0.263 #> 7 nn 2000-01-07 0.262 #> 8 nn 2000-01-08 0.261 #> 9 nn 2000-01-09 0.261 #> 10 nn 2000-01-10 0.260 #> # ℹ 4,312 more rows"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract fitted values from a fable model — fitted.LOGISTIC","title":"Extract fitted values from a fable model — fitted.LOGISTIC","text":"Extracts fitted values.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract fitted values from a fable model — fitted.LOGISTIC","text":"","code":"# S3 method for LOGISTIC fitted(object, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract fitted values from a fable model — fitted.LOGISTIC","text":"object Fitted model ... arguments ignored","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/fitted.LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract fitted values from a fable model — fitted.LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) |> fitted() #> # A tsibble: 4,322 x 3 [1D] #> # Key: .model [1] #> .model Date .fitted #> #> 1 logistic 2000-01-01 0.261 #> 2 logistic 2000-01-02 0.261 #> 3 logistic 2000-01-03 0.260 #> 4 logistic 2000-01-04 0.259 #> 5 logistic 2000-01-05 0.259 #> 6 logistic 2000-01-06 0.258 #> 7 logistic 2000-01-07 0.258 #> 8 logistic 2000-01-08 0.258 #> 9 logistic 2000-01-09 0.257 #> 10 logistic 2000-01-10 0.257 #> # ℹ 4,312 more rows"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Forecast a model from the fable package — forecast.BINNET","title":"Forecast a model from the fable package — forecast.BINNET","text":"Produces forecasts trained model.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forecast a model from the fable package — forecast.BINNET","text":"","code":"# S3 method for BINNET forecast(object, new_data, specials = NULL, simulate = TRUE, times = 5000, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forecast a model from the fable package — forecast.BINNET","text":"object model forecasts required. new_data tsibble containing time points exogenous regressors produce forecasts . specials (passed fabletools::forecast.mdl_df()). simulate TRUE, forecast distributions computed using simulation Bernoulli model. times number sample paths use estimating forecast distribution simulate = TRUE. ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forecast a model from the fable package — forecast.BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) |> forecast(times = 10) #> # A fable: 14 x 4 [1D] #> # Key: .model [1] #> .model Date Wet .mean #> #> 1 nn 2011-11-01 sample[10] 0.4 #> 2 nn 2011-11-02 sample[10] 0.4 #> 3 nn 2011-11-03 sample[10] 0.6 #> 4 nn 2011-11-04 sample[10] 0.7 #> 5 nn 2011-11-05 sample[10] 0.3 #> 6 nn 2011-11-06 sample[10] 0.4 #> 7 nn 2011-11-07 sample[10] 0.7 #> 8 nn 2011-11-08 sample[10] 0.3 #> 9 nn 2011-11-09 sample[10] 0.2 #> 10 nn 2011-11-10 sample[10] 0.5 #> 11 nn 2011-11-11 sample[10] 0.5 #> 12 nn 2011-11-12 sample[10] 0.3 #> 13 nn 2011-11-13 sample[10] 0.3 #> 14 nn 2011-11-14 sample[10] 0.3"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Forecast a model from the fable package — forecast.LOGISTIC","title":"Forecast a model from the fable package — forecast.LOGISTIC","text":"Produces forecasts trained model.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forecast a model from the fable package — forecast.LOGISTIC","text":"","code":"# S3 method for LOGISTIC forecast( object, new_data, specials = NULL, simulate = FALSE, times = 5000, ... )"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forecast a model from the fable package — forecast.LOGISTIC","text":"object model forecasts required. new_data tsibble containing time points exogenous regressors produce forecasts . specials (passed fabletools::forecast.mdl_df()). simulate TRUE, forecast distributions computed using simulation Bernoulli model. times number sample paths use estimating forecast distribution simulate = TRUE. ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/forecast.LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forecast a model from the fable package — forecast.LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) |> forecast(h = \"2 years\") #> # A fable: 730 x 4 [1D] #> # Key: .model [1] #> .model Date Wet .mean #> #> 1 logistic 2011-11-01 B(1, 0.41) 0.413 #> 2 logistic 2011-11-02 B(1, 0.41) 0.412 #> 3 logistic 2011-11-03 B(1, 0.41) 0.411 #> 4 logistic 2011-11-04 B(1, 0.41) 0.410 #> 5 logistic 2011-11-05 B(1, 0.41) 0.409 #> 6 logistic 2011-11-06 B(1, 0.41) 0.408 #> 7 logistic 2011-11-07 B(1, 0.41) 0.407 #> 8 logistic 2011-11-08 B(1, 0.41) 0.405 #> 9 logistic 2011-11-09 B(1, 0.4) 0.404 #> 10 logistic 2011-11-10 B(1, 0.4) 0.402 #> # ℹ 720 more rows"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate new data from a fable model — generate.BINNET","title":"Generate new data from a fable model — generate.BINNET","text":"Simulates future paths dataset using fitted model.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate new data from a fable model — generate.BINNET","text":"","code":"# S3 method for BINNET generate(x, new_data, specials = NULL, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate new data from a fable model — generate.BINNET","text":"x object. new_data tsibble containing time points exogenous regressors produce forecasts . specials (passed fabletools::generate.mdl_df()). ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate new data from a fable model — generate.BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) |> generate() #> # A tsibble: 14 x 4 [1D] #> # Key: .model, .rep [1] #> .model .rep Date .sim #> #> 1 nn 1 2011-11-01 1 #> 2 nn 1 2011-11-02 1 #> 3 nn 1 2011-11-03 0 #> 4 nn 1 2011-11-04 0 #> 5 nn 1 2011-11-05 0 #> 6 nn 1 2011-11-06 1 #> 7 nn 1 2011-11-07 0 #> 8 nn 1 2011-11-08 1 #> 9 nn 1 2011-11-09 0 #> 10 nn 1 2011-11-10 1 #> 11 nn 1 2011-11-11 0 #> 12 nn 1 2011-11-12 0 #> 13 nn 1 2011-11-13 0 #> 14 nn 1 2011-11-14 1"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate new data from a fable model — generate.LOGISTIC","title":"Generate new data from a fable model — generate.LOGISTIC","text":"Simulates future paths dataset using fitted model.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate new data from a fable model — generate.LOGISTIC","text":"","code":"# S3 method for LOGISTIC generate(x, new_data, specials, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate new data from a fable model — generate.LOGISTIC","text":"x object. new_data tsibble containing time points exogenous regressors produce forecasts . specials (passed fabletools::generate.mdl_df()). ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/generate.LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate new data from a fable model — generate.LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) |> generate() #> # A tsibble: 14 x 4 [1D] #> # Key: .model, .rep [1] #> .model .rep Date .sim #> #> 1 logistic 1 2011-11-01 1 #> 2 logistic 1 2011-11-02 0 #> 3 logistic 1 2011-11-03 0 #> 4 logistic 1 2011-11-04 1 #> 5 logistic 1 2011-11-05 1 #> 6 logistic 1 2011-11-06 0 #> 7 logistic 1 2011-11-07 0 #> 8 logistic 1 2011-11-08 1 #> 9 logistic 1 2011-11-09 1 #> 10 logistic 1 2011-11-10 0 #> 11 logistic 1 2011-11-11 1 #> 12 logistic 1 2011-11-12 1 #> 13 logistic 1 2011-11-13 1 #> 14 logistic 1 2011-11-14 1"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Glance a BINNET model — glance.BINNET","title":"Glance a BINNET model — glance.BINNET","text":"Construct single row summary BINNET model. Contains variance residuals (sigma2).","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glance a BINNET model — glance.BINNET","text":"","code":"# S3 method for BINNET glance(x, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glance a BINNET model — glance.BINNET","text":"x model R object convert single-row data frame ... arguments ignored","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.BINNET.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Glance a BINNET model — glance.BINNET","text":"one row tibble summarising model's fit.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Glance a BINNET model — glance.BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) |> glance() #> # A tibble: 1 × 6 #> .model inputs hidden_nodes weights repeats sigma2 #> #> 1 nn 2 2 9 20 0.227"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Glance a LOGISTIC — glance.LOGISTIC","title":"Glance a LOGISTIC — glance.LOGISTIC","text":"Construct single row summary LOGISTIC model.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Glance a LOGISTIC — glance.LOGISTIC","text":"","code":"# S3 method for LOGISTIC glance(x, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Glance a LOGISTIC — glance.LOGISTIC","text":"x model R object convert single-row data frame ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.LOGISTIC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Glance a LOGISTIC — glance.LOGISTIC","text":"one row tibble summarising model's fit.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.LOGISTIC.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Glance a LOGISTIC — glance.LOGISTIC","text":"Contains R squared (r_squared), variance residuals (sigma2), log-likelihood (log_lik), information criterion (AIC, AICc, BIC).","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/glance.LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Glance a LOGISTIC — glance.LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) |> glance() #> # A tibble: 1 × 12 #> .model df log_lik AIC AICc BIC deviance df.residual rank #> #> 1 logistic 11 -2787. 5596. 5596. 5666. 5574. 4311 11 #> # ℹ 3 more variables: null_deviance , df_null , nobs "},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/melb_rain.html","id":null,"dir":"Reference","previous_headings":"","what":"Daily rainfall in Melbourne, Australia — melb_rain","title":"Daily rainfall in Melbourne, Australia — melb_rain","text":"dataset containing daily rainfall amounts 1 January 2000 31 December 2011.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/melb_rain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Daily rainfall in Melbourne, Australia — melb_rain","text":"","code":"melb_rain"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/melb_rain.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Daily rainfall in Melbourne, Australia — melb_rain","text":"tsibble data frame 4322 rows 3 variables.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/melb_rain.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Daily rainfall in Melbourne, Australia — melb_rain","text":"Australian Bureau Meteorology. http://www.bom.gov.au/climate/data/","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. dplyr %>% tsibble as_tsibble","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Refit a BINNET model — refit.BINNET","title":"Refit a BINNET model — refit.BINNET","text":"Applies fitted BINNET model new dataset.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Refit a BINNET model — refit.BINNET","text":"","code":"# S3 method for BINNET refit(object, new_data, specials = NULL, reestimate = FALSE, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Refit a BINNET model — refit.BINNET","text":"object model forecasts required. new_data tsibble containing time points exogenous regressors produce forecasts . specials (passed fabletools::forecast.mdl_df()). reestimate TRUE, networks initialized random starting weights suit new data. FALSE, every network best individual set weights found pre-estimation process used starting weight vector. ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.BINNET.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Refit a BINNET model — refit.BINNET","text":"refitted model.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Refit a LOGISTIC — refit.LOGISTIC","title":"Refit a LOGISTIC — refit.LOGISTIC","text":"Applies fitted LOGISTIC new dataset.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Refit a LOGISTIC — refit.LOGISTIC","text":"","code":"# S3 method for LOGISTIC refit(object, new_data, specials = NULL, reestimate = FALSE, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/refit.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Refit a LOGISTIC — refit.LOGISTIC","text":"object fitted model object. new_data tsibble containing time points exogenous regressors refit required. specials list special functions used model, (passed fabletools::forecast.mdl_df). reestimate TRUE, networks initialized random starting weights suit new data. FALSE, every network best individual set weights found pre-estimation process used starting weight vector. ... arguments passed methods","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract residuals from a fable model. — residuals.BINNET","title":"Extract residuals from a fable model. — residuals.BINNET","text":"Extracts residuals.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract residuals from a fable model. — residuals.BINNET","text":"","code":"# S3 method for BINNET residuals(object, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract residuals from a fable model. — residuals.BINNET","text":"object Fitted model ... arguments ignored","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract residuals from a fable model. — residuals.BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) |> residuals() #> # A tsibble: 4,322 x 3 [1D] #> # Key: .model [1] #> .model Date .resid #> #> 1 nn 2000-01-01 0.735 #> 2 nn 2000-01-02 -0.263 #> 3 nn 2000-01-03 -0.262 #> 4 nn 2000-01-04 0.739 #> 5 nn 2000-01-05 0.740 #> 6 nn 2000-01-06 -0.259 #> 7 nn 2000-01-07 -0.259 #> 8 nn 2000-01-08 -0.258 #> 9 nn 2000-01-09 -0.257 #> 10 nn 2000-01-10 0.743 #> # ℹ 4,312 more rows"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract residuals from a fable model. — residuals.LOGISTIC","title":"Extract residuals from a fable model. — residuals.LOGISTIC","text":"Extracts residuals.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract residuals from a fable model. — residuals.LOGISTIC","text":"","code":"# S3 method for LOGISTIC residuals( object, type = c(\"deviance\", \"innovation\", \"pearson\", \"working\", \"response\", \"partial\"), ... )"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract residuals from a fable model. — residuals.LOGISTIC","text":"object Fitted model type type residuals returned. alternatives : \"deviance\" (default), \"pearson\", \"working\", \"response\", \"partial\". Can abbreviated. ... arguments ignored","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/residuals.LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract residuals from a fable model. — residuals.LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) |> residuals(type = \"deviance\") #> # A tsibble: 4,322 x 3 [1D] #> # Key: .model [1] #> .model Date .resid #> #> 1 logistic 2000-01-01 1.64 #> 2 logistic 2000-01-02 -0.777 #> 3 logistic 2000-01-03 -0.776 #> 4 logistic 2000-01-04 1.64 #> 5 logistic 2000-01-05 1.64 #> 6 logistic 2000-01-06 -0.773 #> 7 logistic 2000-01-07 -0.772 #> 8 logistic 2000-01-08 -0.772 #> 9 logistic 2000-01-09 -0.771 #> 10 logistic 2000-01-10 1.65 #> # ℹ 4,312 more rows"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.BINNET.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy a fable model — tidy.BINNET","title":"Tidy a fable model — tidy.BINNET","text":"Returns coefficients model tibble format.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.BINNET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy a fable model — tidy.BINNET","text":"","code":"# S3 method for BINNET tidy(x, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.BINNET.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy a fable model — tidy.BINNET","text":"x object converted tidy tibble::tibble(). ... Additional arguments tidying method.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.BINNET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy a fable model — tidy.BINNET","text":"","code":"melb_rain |> model(nn = BINNET(Wet ~ fourier(K = 1, period = \"year\"))) |> tidy() #> # A tibble: 0 × 3 #> # ℹ 3 variables: .model , term , estimate "},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.LOGISTIC.html","id":null,"dir":"Reference","previous_headings":"","what":"Tidy a fable model — tidy.LOGISTIC","title":"Tidy a fable model — tidy.LOGISTIC","text":"Returns coefficients model tibble format.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.LOGISTIC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tidy a fable model — tidy.LOGISTIC","text":"","code":"# S3 method for LOGISTIC tidy(x, ...)"},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.LOGISTIC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tidy a fable model — tidy.LOGISTIC","text":"x object converted tidy tibble::tibble(). ... Additional arguments tidying method.","code":""},{"path":"https://pkg.robjhyndman.com/fable.binary/reference/tidy.LOGISTIC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Tidy a fable model — tidy.LOGISTIC","text":"","code":"melb_rain |> model(logistic = LOGISTIC(Wet ~ fourier(K = 5, period = \"year\"))) |> tidy() #> # A tibble: 11 × 6 #> .model term estimate std.error statistic p.value #> #> 1 logistic \"(Intercept)\" -0.573 0.0321 -17.8 3.18e-71 #> 2 logistic \"fourier(K = 5, period = \\\"ye… -0.325 0.0458 -7.10 1.26e-12 #> 3 logistic \"fourier(K = 5, period = \\\"ye… -0.279 0.0451 -6.18 6.25e-10 #> 4 logistic \"fourier(K = 5, period = \\\"ye… -0.0203 0.0455 -0.446 6.56e- 1 #> 5 logistic \"fourier(K = 5, period = \\\"ye… -0.0312 0.0453 -0.688 4.91e- 1 #> 6 logistic \"fourier(K = 5, period = \\\"ye… -0.0696 0.0454 -1.53 1.26e- 1 #> 7 logistic \"fourier(K = 5, period = \\\"ye… -0.0207 0.0454 -0.457 6.48e- 1 #> 8 logistic \"fourier(K = 5, period = \\\"ye… -0.0342 0.0454 -0.754 4.51e- 1 #> 9 logistic \"fourier(K = 5, period = \\\"ye… 0.0224 0.0454 0.494 6.21e- 1 #> 10 logistic \"fourier(K = 5, period = \\\"ye… -0.0188 0.0453 -0.415 6.78e- 1 #> 11 logistic \"fourier(K = 5, period = \\\"ye… 0.00815 0.0453 0.180 8.57e- 1"},{"path":"https://pkg.robjhyndman.com/fable.binary/news/index.html","id":"fablebinary-development-version","dir":"Changelog","previous_headings":"","what":"fable.binary (development version)","title":"fable.binary (development version)","text":"Added LOGISTIC BINNET models","code":""}]