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+
-

The feasts package provides a smorgasbord of tools for analysing tidy temporal data in the tsibble format. The package name is an acronym of its primary functionality: Feature Extraction and Statistics for Time Series.

-

Suppose we wanted to analyse seasonal patterns in the number of domestic travellers to Melbourne, Australia. In the tsibble::tourism data set, this can be further broken down into 4 reasons of travel: “business”, “holiday”, “visiting friends and relatives” and “other reasons”. The first observation from each series are shown below.

+

The feasts package provides a smorgasbord of tools for analysing tidy +temporal data in the tsibble format. The package name is an acronym of +its primary functionality: Feature Extraction and Statistics for +Time Series.

+

Suppose we wanted to analyse seasonal patterns in the number of +domestic travellers to Melbourne, Australia. In the +tsibble::tourism data set, this can be further broken down +into 4 reasons of travel: “business”, “holiday”, “visiting friends and +relatives” and “other reasons”. The first observation from each series +are shown below.

 tourism_melb <- tourism %>%
   filter(Region == "Melbourne")
@@ -130,51 +132,84 @@ 

Introduction to feasts

#> 2 1998 Q1 Melbourne Victoria Holiday 428. #> 3 1998 Q1 Melbourne Victoria Other 79.9 #> 4 1998 Q1 Melbourne Victoria Visiting 666.
-

A useful first look at a time series is with a standard time series plot.

+

A useful first look at a time series is with a standard time series +plot.

 tourism_melb %>%
   autoplot(Trips)

-

This plot reveals that each of the reasons for visiting Melbourne follow a similar pattern, and that in recent years the amount of tourism is increasing. It is also clear that most people travel to Melbourne for one of three reasons (Business, Holiday or Visiting), and fewer travellers report other travel purposes.

-

While this plot reveals many useful large scale features of the data, it can be difficult to identify specifics about the seasonal pattern (other than its existence). Which quarter has the most tourism travel in Melbourne? To find out we will need to investigate other plot types.

+

This plot reveals that each of the reasons for visiting Melbourne +follow a similar pattern, and that in recent years the amount of tourism +is increasing. It is also clear that most people travel to Melbourne for +one of three reasons (Business, Holiday or Visiting), and fewer +travellers report other travel purposes.

+

While this plot reveals many useful large scale features of the data, +it can be difficult to identify specifics about the seasonal pattern +(other than its existence). Which quarter has the most tourism travel in +Melbourne? To find out we will need to investigate other plot types.

 tourism_melb %>%
   gg_season(Trips)

-

The seasonal plot (gg_season()) wraps a seasonal period (in this case, years) over the x axis, allowing you to see how each quarter varies. In particular, it is apparent that Q3 is a low point for people visiting friends and relatives, which noticeably increases in Q4. Similarly Q2 and Q3 are the time periods with the most business travel.

-

The trend of recent years can also be seen in the spread between the lines. The more recent years (purple/pink) are higher than the previous years. This also reveals that the drop in visiting tourism in Q3 was far less extreme in the most recent two years.

+

The seasonal plot (gg_season()) wraps a seasonal period +(in this case, years) over the x axis, allowing you to see how each +quarter varies. In particular, it is apparent that Q3 is a low point for +people visiting friends and relatives, which noticeably increases in Q4. +Similarly Q2 and Q3 are the time periods with the most business +travel.

+

The trend of recent years can also be seen in the spread between the +lines. The more recent years (purple/pink) are higher than the previous +years. This also reveals that the drop in visiting tourism in Q3 was far +less extreme in the most recent two years.

 tourism_melb %>%
   gg_subseries(Trips)

-

An alternative visualisation of seasonal patterns is the subseries plot (gg_subseries()), which isolates seasonal periods into separate plots. The blue lines indicate the average number of trips in each quarter, making the increase in visiting tourism from Q3 to Q4 more obvious. This plot style is especially useful in seeing how seasonality changes over time. Focusing on the visiting tourism (last row of facets), the number of tourists in Q3 and Q4 are increasing much more than in Q1 and Q2 (suggesting that the trend may vary between seasons).

-

A look at the correlations in each series could reveal structures which are difficult to identify in the above plots.

+

An alternative visualisation of seasonal patterns is the subseries +plot (gg_subseries()), which isolates seasonal periods into +separate plots. The blue lines indicate the average number of trips in +each quarter, making the increase in visiting tourism from Q3 to Q4 more +obvious. This plot style is especially useful in seeing how seasonality +changes over time. Focusing on the visiting tourism (last row of +facets), the number of tourists in Q3 and Q4 are increasing much more +than in Q1 and Q2 (suggesting that the trend may vary between +seasons).

+

A look at the correlations in each series could reveal structures +which are difficult to identify in the above plots.

 tourism_melb %>%
   ACF(Trips)
 #> # A tsibble: 76 x 5 [1Q]
 #> # Key:       Region, State, Purpose [4]
-#>    Region    State    Purpose    lag     acf
-#>    <chr>     <chr>    <chr>    <lag>   <dbl>
-#>  1 Melbourne Victoria Business    1Q  0.487 
-#>  2 Melbourne Victoria Business    2Q  0.193 
-#>  3 Melbourne Victoria Business    3Q  0.331 
-#>  4 Melbourne Victoria Business    4Q  0.470 
-#>  5 Melbourne Victoria Business    5Q  0.219 
-#>  6 Melbourne Victoria Business    6Q  0.0750
-#>  7 Melbourne Victoria Business    7Q  0.168 
-#>  8 Melbourne Victoria Business    8Q  0.321 
-#>  9 Melbourne Victoria Business    9Q  0.166 
-#> 10 Melbourne Victoria Business   10Q -0.0183
-#> # … with 66 more rows
-

The above code computes autocorrelations (ACF()), however it is also possible to compute partial autocorrelations (PACF()) and cross-correlations (CCF()).

-

The tables given from these correlation functions also have a nice autoplot() method, which will show the correlations along with a threshold for significance (controllable with the level argument).

+#> Region State Purpose lag acf +#> <chr> <chr> <chr> <cf_lag> <dbl> +#> 1 Melbourne Victoria Business 1Q 0.487 +#> 2 Melbourne Victoria Business 2Q 0.193 +#> 3 Melbourne Victoria Business 3Q 0.331 +#> 4 Melbourne Victoria Business 4Q 0.470 +#> 5 Melbourne Victoria Business 5Q 0.219 +#> 6 Melbourne Victoria Business 6Q 0.0750 +#> 7 Melbourne Victoria Business 7Q 0.168 +#> 8 Melbourne Victoria Business 8Q 0.321 +#> 9 Melbourne Victoria Business 9Q 0.166 +#> 10 Melbourne Victoria Business 10Q -0.0183 +#> # ℹ 66 more rows
+

The above code computes autocorrelations (ACF()), +however it is also possible to compute partial autocorrelations +(PACF()) and cross-correlations (CCF()).

+

The tables given from these correlation functions also have a nice +autoplot() method, which will show the correlations along +with a threshold for significance (controllable with the +level argument).

 tourism_melb %>%
   ACF(Trips) %>%
   autoplot()

-

Another helpful strategy in investigating the patterns in a time series is to decompose it into components of interest. A useful decomposition for this is the STL decomposition, which allows you to extract multiple seasonal patterns with any seasonal period.

+

Another helpful strategy in investigating the patterns in a time +series is to decompose it into components of interest. A useful +decomposition for this is the STL decomposition, which allows you to +extract multiple seasonal patterns with any seasonal period.

 tourism_melb %>%
   model(STL(Trips ~ season(window = "periodic"))) %>% 
@@ -182,45 +217,62 @@ 

Introduction to feasts

#> # A dable: 320 x 10 [1Q] #> # Key: Region, State, Purpose, .model [4] #> # : Trips = trend + season_year + remainder -#> Region State Purpose .model Quarter Trips trend seaso…¹ remai…² seaso…³ -#> <chr> <chr> <chr> <chr> <qtr> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 Melbourne Victoria Busine… "STL(… 1998 Q1 405. 437. -58.0 25.8 463. -#> 2 Melbourne Victoria Busine… "STL(… 1998 Q2 408. 434. 18.1 -44.1 390. -#> 3 Melbourne Victoria Busine… "STL(… 1998 Q3 486. 430. 37.9 18.3 448. -#> 4 Melbourne Victoria Busine… "STL(… 1998 Q4 429. 431. 1.99 -3.48 427. -#> 5 Melbourne Victoria Busine… "STL(… 1999 Q1 361. 427. -58.0 -7.82 419. -#> 6 Melbourne Victoria Busine… "STL(… 1999 Q2 486. 409. 18.1 58.6 468. -#> 7 Melbourne Victoria Busine… "STL(… 1999 Q3 359. 418. 37.9 -97.0 321. -#> 8 Melbourne Victoria Busine… "STL(… 1999 Q4 426. 440. 1.99 -16.7 424. -#> 9 Melbourne Victoria Busine… "STL(… 2000 Q1 495. 468. -58.0 84.1 552. -#> 10 Melbourne Victoria Busine… "STL(… 2000 Q2 499. 501. 18.1 -19.8 481. -#> # … with 310 more rows, and abbreviated variable names ¹​season_year, -#> # ²​remainder, ³​season_adjust
-

The above call to STL() has decomposed the Trips variable into three components such that Trips = trend + season_year + remainder. By setting season(window = "periodic"), we have set the seasonal pattern to be unchanging — you can control how quickly the seasonal pattern can change by setting this to some number (smaller numbers correspond to more rapid change).

+#> Region State Purpose .model Quarter Trips trend season_year remainder +#> <chr> <chr> <chr> <chr> <qtr> <dbl> <dbl> <dbl> <dbl> +#> 1 Melbourne Victoria Business "STL(T… 1998 Q1 405. 437. -58.0 25.8 +#> 2 Melbourne Victoria Business "STL(T… 1998 Q2 408. 434. 18.1 -44.1 +#> 3 Melbourne Victoria Business "STL(T… 1998 Q3 486. 430. 37.9 18.3 +#> 4 Melbourne Victoria Business "STL(T… 1998 Q4 429. 431. 1.99 -3.48 +#> 5 Melbourne Victoria Business "STL(T… 1999 Q1 361. 427. -58.0 -7.82 +#> 6 Melbourne Victoria Business "STL(T… 1999 Q2 486. 409. 18.1 58.6 +#> 7 Melbourne Victoria Business "STL(T… 1999 Q3 359. 418. 37.9 -97.0 +#> 8 Melbourne Victoria Business "STL(T… 1999 Q4 426. 440. 1.99 -16.7 +#> 9 Melbourne Victoria Business "STL(T… 2000 Q1 495. 468. -58.0 84.1 +#> 10 Melbourne Victoria Business "STL(T… 2000 Q2 499. 501. 18.1 -19.8 +#> # ℹ 310 more rows +#> # ℹ 1 more variable: season_adjust <dbl>
+

The above call to STL() has decomposed the +Trips variable into three components such that +Trips = trend + season_year + remainder. By setting +season(window = "periodic"), we have set the seasonal +pattern to be unchanging — you can control how quickly the seasonal +pattern can change by setting this to some number (smaller numbers +correspond to more rapid change).

 tourism_melb %>%
   model(STL(Trips ~ season(window = 9))) %>%
   components() %>% 
   autoplot()

-

Much like the table from ACF(), decompositions can also be plotted with autoplot(). This gives the expected faceted plot of the components extracted from the measured variable. The plot shows that each purpose of travel has a different seasonal pattern, and that the strength and structure of this pattern has changed over time. As these components are often on different scales, this plot includes a set of scale bars which are of equal scaled size across all plots.

-

The above plots and analysis are useful for if you’re looking at a few series, but what can be done if you needed to look at and compare more time series? Extracting features from a collection of time series is a scalable approach to analysing many data sets. Each feature is a numerical summary of the data set’s defining characteristics, and a set of features can be created using feature_set().

+

Much like the table from ACF(), decompositions can also +be plotted with autoplot(). This gives the expected faceted +plot of the components extracted from the measured variable. The plot +shows that each purpose of travel has a different seasonal pattern, and +that the strength and structure of this pattern has changed over time. +As these components are often on different scales, this plot includes a +set of scale bars which are of equal scaled size across all plots.

+

The above plots and analysis are useful for if you’re looking at a +few series, but what can be done if you needed to look at and compare +more time series? Extracting features from a collection of time series +is a scalable approach to analysing many data sets. Each feature is a +numerical summary of the data set’s defining characteristics, and a set +of features can be created using feature_set().

 tourism_melb_features <- tourism_melb %>%
   features(Trips, feature_set(tags = "stl"))
 tourism_melb_features
 #> # A tibble: 4 × 12
-#>   Region   State Purpose trend…¹ seaso…² seaso…³ seaso…⁴ spiki…⁵ linea…⁶ curva…⁷
-#>   <chr>    <chr> <chr>     <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-#> 1 Melbour… Vict… Busine…   0.782   0.559       3       1   515.     351.    326.
-#> 2 Melbour… Vict… Holiday   0.864   0.308       1       0   626.     793.    121.
-#> 3 Melbour… Vict… Other     0.826   0.183       3       0    11.8    200.    104.
-#> 4 Melbour… Vict… Visiti…   0.759   0.475       0       3   932.     457.    320.
-#> # … with 2 more variables: stl_e_acf1 <dbl>, stl_e_acf10 <dbl>, and abbreviated
-#> #   variable names ¹​trend_strength, ²​seasonal_strength_year,
-#> #   ³​seasonal_peak_year, ⁴​seasonal_trough_year, ⁵​spikiness, ⁶​linearity,
-#> #   ⁷​curvature
-

In the example above, the components from the STL decomposition has been used to summarise the strength of trend and seasonality components of each series.

+#> Region State Purpose trend_strength seasonal_strength_year seasonal_peak_year +#> <chr> <chr> <chr> <dbl> <dbl> <dbl> +#> 1 Melbou… Vict… Busine… 0.782 0.559 3 +#> 2 Melbou… Vict… Holiday 0.864 0.308 1 +#> 3 Melbou… Vict… Other 0.826 0.183 3 +#> 4 Melbou… Vict… Visiti… 0.759 0.475 0 +#> # ℹ 6 more variables: seasonal_trough_year <dbl>, spikiness <dbl>, +#> # linearity <dbl>, curvature <dbl>, stl_e_acf1 <dbl>, stl_e_acf10 <dbl>
+

In the example above, the components from the STL decomposition has +been used to summarise the strength of trend and seasonality components +of each series.

These features are particularly useful to show on a plot.

 library(ggplot2)
@@ -230,7 +282,13 @@ 

Introduction to feasts

coord_equal() + lims(x = c(0,1), y = c(0,1))

-

When analysing just four series, a plot of the features does not look very exciting. It is worth noting that a lot of the individuality seen in the previous analysis have been lost when each series is summarised down to just two values. However recall that the analysis has been working from a very small subset of the complete tourism data set. Let’s see how Melbourne compares with the other regions in the data.

+

When analysing just four series, a plot of the features does not look +very exciting. It is worth noting that a lot of the individuality seen +in the previous analysis have been lost when each series is summarised +down to just two values. However recall that the analysis has been +working from a very small subset of the complete tourism +data set. Let’s see how Melbourne compares with the other regions in the +data.

 tourism_features <- tourism %>%
   features(Trips, feat_stl)
@@ -242,8 +300,11 @@ 

Introduction to feasts

facet_wrap(vars(Purpose)) + lims(x = c(0,1), y = c(0,1))

-

Looks like Melbourne is one of the trendiest places around Australia!

-

More information about time series analysis using the feasts package can be found in Forecasting: Principles and Practices (3rd Ed.) and in the pkgdown site.

+

Looks like Melbourne is one of the trendiest places around +Australia!

+

More information about time series analysis using the feasts package +can be found in Forecasting: +Principles and Practices (3rd Ed.) and in the pkgdown site.

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/authors.html b/dev/authors.html index b3297dfa..483f7180 100644 --- a/dev/authors.html +++ b/dev/authors.html @@ -2,13 +2,7 @@ Authors and Citation • feasts +
@@ -23,7 +17,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -104,15 +98,16 @@

Citation

-

O'Hara-Wild M, Hyndman R, Wang E (2022). +

O'Hara-Wild M, Hyndman R, Wang E (2024). feasts: Feature Extraction and Statistics for Time Series. -http://feasts.tidyverts.org/, https://github.com/tidyverts/feasts/. +R package version 0.3.2.9000, https://github.com/tidyverts/feasts/, http://feasts.tidyverts.org/.

@Manual{,
   title = {feasts: Feature Extraction and Statistics for Time Series},
   author = {Mitchell O'Hara-Wild and Rob Hyndman and Earo Wang},
-  year = {2022},
-  note = {http://feasts.tidyverts.org/, https://github.com/tidyverts/feasts/},
+  year = {2024},
+  note = {R package version 0.3.2.9000, https://github.com/tidyverts/feasts/},
+  url = {http://feasts.tidyverts.org/},
 }
@@ -126,7 +121,7 @@

Citation

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/favicon-16x16.png b/dev/favicon-16x16.png index eaf6c083..fdb3a3f3 100644 Binary files a/dev/favicon-16x16.png and b/dev/favicon-16x16.png differ diff --git a/dev/favicon-32x32.png b/dev/favicon-32x32.png index 2257b19e..3a719f58 100644 Binary files a/dev/favicon-32x32.png and b/dev/favicon-32x32.png differ diff --git a/dev/index.html b/dev/index.html index c9fe0a1b..43e4edee 100644 --- a/dev/index.html +++ b/dev/index.html @@ -27,13 +27,7 @@ + @@ -50,7 +44,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000 @@ -105,7 +99,7 @@ - +

R build status Coverage status CRAN_Status_Badge Lifecycle: maturing

Overview

@@ -272,15 +266,7 @@

Developers

-
-

Dev status

- -
+ @@ -293,7 +279,7 @@

Dev status

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/news/index.html b/dev/news/index.html index 784d649a..fa951e6f 100644 --- a/dev/news/index.html +++ b/dev/news/index.html @@ -2,13 +2,7 @@ Changelog • feasts +
@@ -23,7 +17,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -70,12 +64,32 @@

Changelog

Source: NEWS.md -
+
+ +
+
+ +

Minor patch to resolve CRAN check issues with ggplot2 v3.5.0 breaking changes.

+
+

Improvements

+
+
+

Bug fixes

+
+
+
+ +

Minor patch to resolve CRAN check issues with S3 method consistency.

+

New features

-
+

Improvements

+
@@ -119,13 +134,16 @@

New features

-
  • Added X_13ARIMA_SEATS() decomposition method. This is a complete wrapper of the X-13ARIMA-SEATS developed by the U.S. Census Bureau, implemented via the seasonal::seas() function. The defaults match what is used in the seasonal pacakge, however these defaults can be removed (giving an empty default model) by setting defaults="none".
+

Breaking changes

-
+

Improvements

-
+
@@ -147,7 +165,8 @@
+gg_season() labels are low aligned outward (#115). +

Bug fixes

Breaking changes

-
  • The n_flat_spots() return name is now “longest_flat_spot” to better describe the feature.
+

Bug fixes

  • Fixed spectral density plot in gg_tsdisplay() erroring when the spec.ar order is chosen to be 0.
  • @@ -183,7 +203,8 @@

Improvements

  • Added partial matching of the type argument in ACF().
  • @@ -191,7 +212,8 @@

    Improvements

Bug fixes

-
  • Fixed the minimum data length for seasonal estimation in feat_stl().
+
  • Fixed the minimum data length for seasonal estimation in feat_stl().
  • +
@@ -220,7 +242,8 @@

Improvements

Breaking changes

-
  • Decompositions are now treated as models. To access the decomposed values, you will now have to use components(). For example, tourism %>% STL(Trips) is now tourism %>% model(STL(Trips)) %>% components(). This change allows for more flexible decomposition specifications, and better interfaces for decomposition modelling.
+
  • Decompositions are now treated as models. To access the decomposed values, you will now have to use components(). For example, tourism %>% STL(Trips) is now tourism %>% model(STL(Trips)) %>% components(). This change allows for more flexible decomposition specifications, and better interfaces for decomposition modelling.
  • +

Bug fixes

  • Fixed bug with feat_spectral() not showing results.
  • @@ -234,14 +257,17 @@

    Bug fixesfeasts 0.1.12019-09-02

    Improvements

    -
    • Better naming of seasonal columns in STL decomposition when seasonal period is specified.
    +
    • Better naming of seasonal columns in STL decomposition when seasonal period is specified.
    • +

Bug fixes

-
  • Fixes issues with running tests on unsupported systems.
+
  • Fixes issues with running tests on unsupported systems.
  • +
-
  • First release.
+
  • First release.
  • +

New features

  • Added support for graphical analysis of tidy temporal data and models, with gg_season, gg_subseries, gg_lag, gg_tsdisplay, gg_tsresiduals, gg_arma.
  • Added support for autocorrelation functions and plots, with ACF, PACF, CCF, and autoplot.tbl_cf @@ -266,7 +292,7 @@

    New features
    -

    Site built with pkgdown 2.0.6.

    +

    Site built with pkgdown 2.0.7.

diff --git a/dev/pkgdown.yml b/dev/pkgdown.yml index bf0b5fdc..e35b6960 100644 --- a/dev/pkgdown.yml +++ b/dev/pkgdown.yml @@ -1,9 +1,9 @@ -pandoc: 2.7.3 -pkgdown: 2.0.6 +pandoc: 3.1.11 +pkgdown: 2.0.7 pkgdown_sha: ~ articles: feasts: feasts.html -last_built: 2022-09-02T01:13Z +last_built: 2024-03-15T09:23Z urls: reference: https://feasts.tidyverts.org/reference article: https://feasts.tidyverts.org/articles diff --git a/dev/reference/ACF-1.png b/dev/reference/ACF-1.png index f8f752ff..7a354328 100644 Binary files a/dev/reference/ACF-1.png and b/dev/reference/ACF-1.png differ diff --git a/dev/reference/ACF-2.png b/dev/reference/ACF-2.png index 60088695..f0234e2e 100644 Binary files a/dev/reference/ACF-2.png and b/dev/reference/ACF-2.png differ diff --git a/dev/reference/ACF-3.png b/dev/reference/ACF-3.png index e94a6098..cc85736e 100644 Binary files a/dev/reference/ACF-3.png and b/dev/reference/ACF-3.png differ diff --git a/dev/reference/ACF.html b/dev/reference/ACF.html index 79745a2e..b7e5f29d 100644 --- a/dev/reference/ACF.html +++ b/dev/reference/ACF.html @@ -6,13 +6,7 @@ from a tsibble."> +
@@ -27,7 +21,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -205,38 +199,38 @@

Examples

vic_elec %>% ACF(Temperature) #> # A tsibble: 47 x 2 [30m] -#> lag acf -#> <lag> <dbl> -#> 1 30m 0.994 -#> 2 60m 0.982 -#> 3 90m 0.967 -#> 4 120m 0.948 -#> 5 150m 0.925 -#> 6 180m 0.901 -#> 7 210m 0.873 -#> 8 240m 0.845 -#> 9 270m 0.815 -#> 10 300m 0.785 -#> # … with 37 more rows +#> lag acf +#> <cf_lag> <dbl> +#> 1 30m 0.994 +#> 2 60m 0.982 +#> 3 90m 0.967 +#> 4 120m 0.948 +#> 5 150m 0.925 +#> 6 180m 0.901 +#> 7 210m 0.873 +#> 8 240m 0.845 +#> 9 270m 0.815 +#> 10 300m 0.785 +#> # ℹ 37 more rows vic_elec %>% ACF(Temperature) %>% autoplot() vic_elec %>% PACF(Temperature) #> # A tsibble: 47 x 2 [30m] -#> lag pacf -#> <lag> <dbl> -#> 1 30m 0.994 -#> 2 60m -0.395 -#> 3 90m -0.220 -#> 4 120m -0.141 -#> 5 150m -0.0911 -#> 6 180m -0.0611 -#> 7 210m -0.0252 -#> 8 240m -0.0101 -#> 9 270m 0.0152 -#> 10 300m 0.0169 -#> # … with 37 more rows +#> lag pacf +#> <cf_lag> <dbl> +#> 1 30m 0.994 +#> 2 60m -0.395 +#> 3 90m -0.220 +#> 4 120m -0.141 +#> 5 150m -0.0911 +#> 6 180m -0.0611 +#> 7 210m -0.0252 +#> 8 240m -0.0101 +#> 9 270m 0.0152 +#> 10 300m 0.0169 +#> # ℹ 37 more rows vic_elec %>% PACF(Temperature) %>% autoplot() @@ -246,19 +240,19 @@

Examples

CCF(GDP, Population) #> # A tsibble: 29 x 3 [1Y] #> # Key: Country [1] -#> Country lag ccf -#> <fct> <lag> <dbl> -#> 1 Australia -14Y 0.394 -#> 2 Australia -13Y 0.427 -#> 3 Australia -12Y 0.461 -#> 4 Australia -11Y 0.496 -#> 5 Australia -10Y 0.530 -#> 6 Australia -9Y 0.565 -#> 7 Australia -8Y 0.601 -#> 8 Australia -7Y 0.637 -#> 9 Australia -6Y 0.674 -#> 10 Australia -5Y 0.711 -#> # … with 19 more rows +#> Country lag ccf +#> <fct> <cf_lag> <dbl> +#> 1 Australia -14Y 0.394 +#> 2 Australia -13Y 0.427 +#> 3 Australia -12Y 0.461 +#> 4 Australia -11Y 0.496 +#> 5 Australia -10Y 0.530 +#> 6 Australia -9Y 0.565 +#> 7 Australia -8Y 0.601 +#> 8 Australia -7Y 0.637 +#> 9 Australia -6Y 0.674 +#> 10 Australia -5Y 0.711 +#> # ℹ 19 more rows global_economy %>% filter(Country == "Australia") %>% @@ -280,7 +274,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/Rplot002.png b/dev/reference/Rplot002.png index 7cc7b350..9eec3174 100644 Binary files a/dev/reference/Rplot002.png and b/dev/reference/Rplot002.png differ diff --git a/dev/reference/Rplot003.png b/dev/reference/Rplot003.png index 900ab2e1..10670800 100644 Binary files a/dev/reference/Rplot003.png and b/dev/reference/Rplot003.png differ diff --git a/dev/reference/STL.html b/dev/reference/STL.html index fba16245..b14b5c71 100644 --- a/dev/reference/STL.html +++ b/dev/reference/STL.html @@ -8,13 +8,7 @@ Unlike stl, mstl is completely automated."> +
@@ -29,7 +23,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -156,20 +150,19 @@

Examples

#> # A dable: 72 x 7 [1M] #> # Key: .model [1] #> # : value = trend + season_year + remainder -#> .model index value trend season…¹ remai…² seaso…³ -#> <chr> <mth> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 STL(value ~ trend(window = 10)) 1973 Jan 9007 9676. -792. 123. 9799. -#> 2 STL(value ~ trend(window = 10)) 1973 Feb 8106 9691. -1510. -75.1 9616. -#> 3 STL(value ~ trend(window = 10)) 1973 Mar 8928 9706. -707. -70.7 9635. -#> 4 STL(value ~ trend(window = 10)) 1973 Apr 9137 9720. -522. -61.2 9659. -#> 5 STL(value ~ trend(window = 10)) 1973 May 10017 9733. 325. -41.3 9692. -#> 6 STL(value ~ trend(window = 10)) 1973 Jun 10826 9753. 843. 229. 9983. -#> 7 STL(value ~ trend(window = 10)) 1973 Jul 11317 9764. 1617. -64.5 9700. -#> 8 STL(value ~ trend(window = 10)) 1973 Aug 10744 9734. 981. 28.4 9763. -#> 9 STL(value ~ trend(window = 10)) 1973 Sep 9713 9638. -105. 181. 9818. -#> 10 STL(value ~ trend(window = 10)) 1973 Oct 9938 9473. 232. 233. 9706. -#> # … with 62 more rows, and abbreviated variable names ¹​season_year, ²​remainder, -#> # ³​season_adjust +#> .model index value trend season_year remainder season_adjust +#> <chr> <mth> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 STL(value ~ trend(w… 1973 Jan 9007 9676. -792. 123. 9799. +#> 2 STL(value ~ trend(w… 1973 Feb 8106 9691. -1510. -75.1 9616. +#> 3 STL(value ~ trend(w… 1973 Mar 8928 9706. -707. -70.7 9635. +#> 4 STL(value ~ trend(w… 1973 Apr 9137 9720. -522. -61.2 9659. +#> 5 STL(value ~ trend(w… 1973 May 10017 9733. 325. -41.3 9692. +#> 6 STL(value ~ trend(w… 1973 Jun 10826 9753. 843. 229. 9983. +#> 7 STL(value ~ trend(w… 1973 Jul 11317 9764. 1617. -64.5 9700. +#> 8 STL(value ~ trend(w… 1973 Aug 10744 9734. 981. 28.4 9763. +#> 9 STL(value ~ trend(w… 1973 Sep 9713 9638. -105. 181. 9818. +#> 10 STL(value ~ trend(w… 1973 Oct 9938 9473. 232. 233. 9706. +#> # ℹ 62 more rows @@ -185,7 +178,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/X_13ARIMA_SEATS.html b/dev/reference/X_13ARIMA_SEATS.html index 896e3ec4..3b6107cb 100644 --- a/dev/reference/X_13ARIMA_SEATS.html +++ b/dev/reference/X_13ARIMA_SEATS.html @@ -3,13 +3,7 @@ the U.S. Census Bureau."> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -198,7 +192,7 @@

forecastmaxlead argument, and the number of backcasts in the maxback argument.

+forecast(...)
...Arguments described in the reference manual linked below.
@@ -387,7 +381,7 @@

Examples

#> 8 X_13ARIMA_SEATS(Beer) 1957 Q4 320 261. 1.19 1.03 268. #> 9 X_13ARIMA_SEATS(Beer) 1958 Q1 272 262. 1.04 1.00 262. #> 10 X_13ARIMA_SEATS(Beer) 1958 Q2 233 262. 0.864 1.03 270. -#> # … with 208 more rows +#> # ℹ 208 more rows # Additive X-11 decomposition fit <- tsibbledata::aus_production %>% @@ -413,20 +407,19 @@

Examples

#> # A dable: 218 x 7 [1Q] #> # Key: .model [1] #> # : Beer = trend + seasonal + irregular -#> .model Quarter Beer trend seaso…¹ irreg…² seaso…³ -#> <chr> <qtr> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 "X_13ARIMA_SEATS(Beer ~ transfor… 1956 Q1 284 272. 6.36 5.66 278. -#> 2 "X_13ARIMA_SEATS(Beer ~ transfor… 1956 Q2 213 255. -35.7 -6.71 249. -#> 3 "X_13ARIMA_SEATS(Beer ~ transfor… 1956 Q3 227 249. -23.7 1.62 251. -#> 4 "X_13ARIMA_SEATS(Beer ~ transfor… 1956 Q4 308 252. 53.0 2.56 255. -#> 5 "X_13ARIMA_SEATS(Beer ~ transfor… 1957 Q1 262 257. 6.69 -2.18 255. -#> 6 "X_13ARIMA_SEATS(Beer ~ transfor… 1957 Q2 228 261. -36.4 3.92 264. -#> 7 "X_13ARIMA_SEATS(Beer ~ transfor… 1957 Q3 236 263. -22.8 -4.00 259. -#> 8 "X_13ARIMA_SEATS(Beer ~ transfor… 1957 Q4 320 265. 52.3 2.25 268. -#> 9 "X_13ARIMA_SEATS(Beer ~ transfor… 1958 Q1 272 267. 7.11 -1.94 265. -#> 10 "X_13ARIMA_SEATS(Beer ~ transfor… 1958 Q2 233 266. -37.3 4.00 270. -#> # … with 208 more rows, and abbreviated variable names ¹​seasonal, ²​irregular, -#> # ³​season_adjust +#> .model Quarter Beer trend seasonal irregular season_adjust +#> <chr> <qtr> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 "X_13ARIMA_SEATS(Beer ~… 1956 Q1 284 272. 6.36 5.66 278. +#> 2 "X_13ARIMA_SEATS(Beer ~… 1956 Q2 213 255. -35.7 -6.71 249. +#> 3 "X_13ARIMA_SEATS(Beer ~… 1956 Q3 227 249. -23.7 1.62 251. +#> 4 "X_13ARIMA_SEATS(Beer ~… 1956 Q4 308 252. 53.0 2.56 255. +#> 5 "X_13ARIMA_SEATS(Beer ~… 1957 Q1 262 257. 6.69 -2.18 255. +#> 6 "X_13ARIMA_SEATS(Beer ~… 1957 Q2 228 261. -36.4 3.92 264. +#> 7 "X_13ARIMA_SEATS(Beer ~… 1957 Q3 236 263. -22.8 -4.00 259. +#> 8 "X_13ARIMA_SEATS(Beer ~… 1957 Q4 320 265. 52.3 2.25 268. +#> 9 "X_13ARIMA_SEATS(Beer ~… 1958 Q1 272 267. 7.11 -1.94 265. +#> 10 "X_13ARIMA_SEATS(Beer ~… 1958 Q2 233 266. -37.3 4.00 270. +#> # ℹ 208 more rows # } @@ -444,7 +437,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/autoplot.tbl_cf.html b/dev/reference/autoplot.tbl_cf.html index e4c79c4e..d74ea7ec 100644 --- a/dev/reference/autoplot.tbl_cf.html +++ b/dev/reference/autoplot.tbl_cf.html @@ -2,13 +2,7 @@ Auto- and Cross- Covariance and -Correlation plots — autoplot.tbl_cf • feasts +
@@ -23,7 +17,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -113,7 +107,7 @@

Value

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/classical_decomposition.html b/dev/reference/classical_decomposition.html index cf935813..fdb39eae 100644 --- a/dev/reference/classical_decomposition.html +++ b/dev/reference/classical_decomposition.html @@ -4,13 +4,7 @@ seasonal component."> +
@@ -25,7 +19,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -142,19 +136,19 @@

Examples

#> # A dable: 72 x 7 [1M] #> # Key: .model [1] #> # : value = trend + seasonal + random -#> .model index value trend seasonal random season_…¹ -#> <chr> <mth> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 classical_decomposition(value) 1973 Jan 9007 NA -806. NA 9813. -#> 2 classical_decomposition(value) 1973 Feb 8106 NA -1523. NA 9629. -#> 3 classical_decomposition(value) 1973 Mar 8928 NA -741. NA 9669. -#> 4 classical_decomposition(value) 1973 Apr 9137 NA -515. NA 9652. -#> 5 classical_decomposition(value) 1973 May 10017 NA 340. NA 9677. -#> 6 classical_decomposition(value) 1973 Jun 10826 NA 745. NA 10081. -#> 7 classical_decomposition(value) 1973 Jul 11317 9599. 1679. 38.2 9638. -#> 8 classical_decomposition(value) 1973 Aug 10744 9500. 986. 258. 9758. -#> 9 classical_decomposition(value) 1973 Sep 9713 9416. -109. 406. 9822. -#> 10 classical_decomposition(value) 1973 Oct 9938 9349. 264. 325. 9674. -#> # … with 62 more rows, and abbreviated variable name ¹​season_adjust +#> .model index value trend seasonal random season_adjust +#> <chr> <mth> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 classical_decomposition(v… 1973 Jan 9007 NA -806. NA 9813. +#> 2 classical_decomposition(v… 1973 Feb 8106 NA -1523. NA 9629. +#> 3 classical_decomposition(v… 1973 Mar 8928 NA -741. NA 9669. +#> 4 classical_decomposition(v… 1973 Apr 9137 NA -515. NA 9652. +#> 5 classical_decomposition(v… 1973 May 10017 NA 340. NA 9677. +#> 6 classical_decomposition(v… 1973 Jun 10826 NA 745. NA 10081. +#> 7 classical_decomposition(v… 1973 Jul 11317 9599. 1679. 38.2 9638. +#> 8 classical_decomposition(v… 1973 Aug 10744 9500. 986. 258. 9758. +#> 9 classical_decomposition(v… 1973 Sep 9713 9416. -109. 406. 9822. +#> 10 classical_decomposition(v… 1973 Oct 9938 9349. 264. 325. 9674. +#> # ℹ 62 more rows as_tsibble(USAccDeaths) %>% model(classical_decomposition(value ~ season(12), type = "mult")) %>% @@ -162,19 +156,19 @@

Examples

#> # A dable: 72 x 7 [1M] #> # Key: .model [1] #> # : value = trend * seasonal * random -#> .model index value trend seaso…¹ random seaso…² -#> <chr> <mth> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 "classical_decomposition(value ~… 1973 Jan 9007 NA 0.908 NA 9922. -#> 2 "classical_decomposition(value ~… 1973 Feb 8106 NA 0.825 NA 9829. -#> 3 "classical_decomposition(value ~… 1973 Mar 8928 NA 0.915 NA 9762. -#> 4 "classical_decomposition(value ~… 1973 Apr 9137 NA 0.941 NA 9713. -#> 5 "classical_decomposition(value ~… 1973 May 10017 NA 1.04 NA 9633. -#> 6 "classical_decomposition(value ~… 1973 Jun 10826 NA 1.09 NA 9960. -#> 7 "classical_decomposition(value ~… 1973 Jul 11317 9599. 1.19 0.989 9491. -#> 8 "classical_decomposition(value ~… 1973 Aug 10744 9500. 1.11 1.02 9656. -#> 9 "classical_decomposition(value ~… 1973 Sep 9713 9416. 0.987 1.05 9843. -#> 10 "classical_decomposition(value ~… 1973 Oct 9938 9349. 1.03 1.03 9649. -#> # … with 62 more rows, and abbreviated variable names ¹​seasonal, ²​season_adjust +#> .model index value trend seasonal random season_adjust +#> <chr> <mth> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 "classical_decomposition(… 1973 Jan 9007 NA 0.908 NA 9922. +#> 2 "classical_decomposition(… 1973 Feb 8106 NA 0.825 NA 9829. +#> 3 "classical_decomposition(… 1973 Mar 8928 NA 0.915 NA 9762. +#> 4 "classical_decomposition(… 1973 Apr 9137 NA 0.941 NA 9713. +#> 5 "classical_decomposition(… 1973 May 10017 NA 1.04 NA 9633. +#> 6 "classical_decomposition(… 1973 Jun 10826 NA 1.09 NA 9960. +#> 7 "classical_decomposition(… 1973 Jul 11317 9599. 1.19 0.989 9491. +#> 8 "classical_decomposition(… 1973 Aug 10744 9500. 1.11 1.02 9656. +#> 9 "classical_decomposition(… 1973 Sep 9713 9416. 0.987 1.05 9843. +#> 10 "classical_decomposition(… 1973 Oct 9938 9349. 1.03 1.03 9649. +#> # ℹ 62 more rows @@ -190,7 +184,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/coef_hurst.html b/dev/reference/coef_hurst.html index b1a812b0..a2b070e9 100644 --- a/dev/reference/coef_hurst.html +++ b/dev/reference/coef_hurst.html @@ -3,13 +3,7 @@ of a time series."> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -111,7 +105,7 @@

Author

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/feasts-package.html b/dev/reference/feasts-package.html index 57b93c32..d49b993e 100644 --- a/dev/reference/feasts-package.html +++ b/dev/reference/feasts-package.html @@ -3,13 +3,7 @@ Provides a collection of features, decomposition methods, statistical summaries and graphics functions for the analysing tidy time series data. The package name 'feasts' is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series."> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -107,7 +101,7 @@

Author

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/feat_acf.html b/dev/reference/feat_acf.html index 9d3ad2a7..1e3ee227 100644 --- a/dev/reference/feat_acf.html +++ b/dev/reference/feat_acf.html @@ -3,13 +3,7 @@ original series, first-differenced series and second-differenced series"> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -127,7 +121,7 @@

Author

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/feat_intermittent.html b/dev/reference/feat_intermittent.html index c2d1bb37..4f3b132f 100644 --- a/dev/reference/feat_intermittent.html +++ b/dev/reference/feat_intermittent.html @@ -3,13 +3,7 @@ intermittent data."> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -115,7 +109,7 @@

References

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/feat_pacf.html b/dev/reference/feat_pacf.html index c77a454f..e7ba517d 100644 --- a/dev/reference/feat_pacf.html +++ b/dev/reference/feat_pacf.html @@ -3,13 +3,7 @@ original series, first-differenced series and second-differenced series."> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -127,7 +121,7 @@

Author

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/feat_spectral.html b/dev/reference/feat_spectral.html index d5c4c576..91723d24 100644 --- a/dev/reference/feat_spectral.html +++ b/dev/reference/feat_spectral.html @@ -3,13 +3,7 @@ spectral density, estimated using an AR model."> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -135,7 +129,7 @@

Author

Examples

feat_spectral(rnorm(1000))
 #> spectral_entropy 
-#>        0.9994038 
+#>                1 
 feat_spectral(lynx)
 #> spectral_entropy 
 #>        0.7331515 
@@ -156,7 +150,7 @@ 

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/feat_stl.html b/dev/reference/feat_stl.html index 9565671d..1d2176ef 100644 --- a/dev/reference/feat_stl.html +++ b/dev/reference/feat_stl.html @@ -3,13 +3,7 @@ time series. This includes details about the strength of trend and seasonality."> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -122,7 +116,7 @@

See also

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/generate.stl_decomposition.html b/dev/reference/generate.stl_decomposition.html index be0efd58..d9a4a630 100644 --- a/dev/reference/generate.stl_decomposition.html +++ b/dev/reference/generate.stl_decomposition.html @@ -4,13 +4,7 @@ any generated data out of the trained sample will produce NA simulations."> +
@@ -25,7 +19,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -91,15 +85,15 @@

Arguments

new_data
-

A tsibble containing future information used to forecast.

+

A tsibble containing the time points and exogenous regressors to produce forecasts for.

specials
-

(passed by fabletools::forecast.mdl_df()).

+

(passed by fabletools::forecast.mdl_df()).

...
-

Additional arguments for forecast model methods.

+

Other arguments passed to methods

@@ -116,17 +110,17 @@

Examples

#> # Key: .model, .rep [3] #> .model .rep index value .sim #> <chr> <chr> <mth> <dbl> <dbl> -#> 1 STL(log(value)) 1 1973 Jan 9007 9192. -#> 2 STL(log(value)) 1 1973 Feb 8106 8273. -#> 3 STL(log(value)) 1 1973 Mar 8928 8967. -#> 4 STL(log(value)) 1 1973 Apr 9137 9007. -#> 5 STL(log(value)) 1 1973 May 10017 10146. -#> 6 STL(log(value)) 1 1973 Jun 10826 10416. -#> 7 STL(log(value)) 1 1973 Jul 11317 11009. -#> 8 STL(log(value)) 1 1973 Aug 10744 10445. -#> 9 STL(log(value)) 1 1973 Sep 9713 9028. -#> 10 STL(log(value)) 1 1973 Oct 9938 9842. -#> # … with 206 more rows +#> 1 STL(log(value)) 1 1973 Jan 9007 9250. +#> 2 STL(log(value)) 1 1973 Feb 8106 8585. +#> 3 STL(log(value)) 1 1973 Mar 8928 9401. +#> 4 STL(log(value)) 1 1973 Apr 9137 9426. +#> 5 STL(log(value)) 1 1973 May 10017 9950. +#> 6 STL(log(value)) 1 1973 Jun 10826 10023. +#> 7 STL(log(value)) 1 1973 Jul 11317 10720. +#> 8 STL(log(value)) 1 1973 Aug 10744 10441. +#> 9 STL(log(value)) 1 1973 Sep 9713 9466. +#> 10 STL(log(value)) 1 1973 Oct 9938 9148. +#> # ℹ 206 more rows
@@ -142,7 +136,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/gg_arma-1.png b/dev/reference/gg_arma-1.png index cbb479ec..9d80643f 100644 Binary files a/dev/reference/gg_arma-1.png and b/dev/reference/gg_arma-1.png differ diff --git a/dev/reference/gg_arma.html b/dev/reference/gg_arma.html index 60051cc5..afd1face 100644 --- a/dev/reference/gg_arma.html +++ b/dev/reference/gg_arma.html @@ -3,13 +3,7 @@ Inverse roots outside the unit circle are shown in red."> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -129,7 +123,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/gg_lag-1.png b/dev/reference/gg_lag-1.png index fbf5fb60..4362e973 100644 Binary files a/dev/reference/gg_lag-1.png and b/dev/reference/gg_lag-1.png differ diff --git a/dev/reference/gg_lag.html b/dev/reference/gg_lag.html index fb08fa15..12ef69fc 100644 --- a/dev/reference/gg_lag.html +++ b/dev/reference/gg_lag.html @@ -3,13 +3,7 @@ the seasonal period to identify how each season correlates with others."> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -155,7 +149,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/gg_season-1.png b/dev/reference/gg_season-1.png index c76d1e10..37bf3c51 100644 Binary files a/dev/reference/gg_season-1.png and b/dev/reference/gg_season-1.png differ diff --git a/dev/reference/gg_season.html b/dev/reference/gg_season.html index 89c2a701..e89da8cf 100644 --- a/dev/reference/gg_season.html +++ b/dev/reference/gg_season.html @@ -5,13 +5,7 @@ and is especially useful in identifying years in which the pattern changes."> +
@@ -26,7 +20,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -89,7 +83,7 @@

Seasonal plot

facet_period = NULL, max_col = Inf, max_col_discrete = 7, - pal = (scales::hue_pal())(9), + pal = (scales::hue_pal())(9), polar = FALSE, labels = c("none", "left", "right", "both"), labels_repel = FALSE, @@ -192,7 +186,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/gg_subseries-1.png b/dev/reference/gg_subseries-1.png index 15f19bc3..c9553814 100644 Binary files a/dev/reference/gg_subseries-1.png and b/dev/reference/gg_subseries-1.png differ diff --git a/dev/reference/gg_subseries.html b/dev/reference/gg_subseries.html index 6a870ae1..1f3fcc39 100644 --- a/dev/reference/gg_subseries.html +++ b/dev/reference/gg_subseries.html @@ -7,13 +7,7 @@ given as an example below."> +
@@ -28,7 +22,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -153,7 +147,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/gg_tsdisplay-1.png b/dev/reference/gg_tsdisplay-1.png index ca67d356..38434e16 100644 Binary files a/dev/reference/gg_tsdisplay-1.png and b/dev/reference/gg_tsdisplay-1.png differ diff --git a/dev/reference/gg_tsdisplay.html b/dev/reference/gg_tsdisplay.html index 827b137a..8c3028f3 100644 --- a/dev/reference/gg_tsdisplay.html +++ b/dev/reference/gg_tsdisplay.html @@ -3,13 +3,7 @@ graphic of either a PACF, histogram, lagged scatterplot or spectral density."> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -158,7 +152,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/gg_tsresiduals-1.png b/dev/reference/gg_tsresiduals-1.png index 5ab221ae..51be0781 100644 Binary files a/dev/reference/gg_tsresiduals-1.png and b/dev/reference/gg_tsresiduals-1.png differ diff --git a/dev/reference/gg_tsresiduals.html b/dev/reference/gg_tsresiduals.html index 00c42226..6f43dd52 100644 --- a/dev/reference/gg_tsresiduals.html +++ b/dev/reference/gg_tsresiduals.html @@ -2,13 +2,7 @@ Ensemble of time series residual diagnostic plots — gg_tsresiduals • feasts +
@@ -23,7 +17,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -135,7 +129,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/guerrero.html b/dev/reference/guerrero.html index 1103ab50..c70551ce 100644 --- a/dev/reference/guerrero.html +++ b/dev/reference/guerrero.html @@ -3,13 +3,7 @@ coefficient of variation for subseries of x."> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -132,7 +126,7 @@

References

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/index.html b/dev/reference/index.html index a7c665f6..095cacf3 100644 --- a/dev/reference/index.html +++ b/dev/reference/index.html @@ -2,13 +2,7 @@ Function reference • feasts +
@@ -23,7 +17,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -147,7 +141,7 @@

Autocorrelation analysis

Unit root tests

-

Unit root tests for use with features().

+

Unit root tests for use with fabletools::features().

unitroot_kpss() unitroot_pp()

@@ -230,7 +224,7 @@

Other features
-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/longest_flat_spot.html b/dev/reference/longest_flat_spot.html index 5340974a..b87cb7d8 100644 --- a/dev/reference/longest_flat_spot.html +++ b/dev/reference/longest_flat_spot.html @@ -4,13 +4,7 @@ single interval.'> +
@@ -25,7 +19,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -112,7 +106,7 @@

Author

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/n_crossing_points.html b/dev/reference/n_crossing_points.html index 27710b0b..3e9be34b 100644 --- a/dev/reference/n_crossing_points.html +++ b/dev/reference/n_crossing_points.html @@ -2,13 +2,7 @@ Number of crossing points — n_crossing_points • feasts +
@@ -23,7 +17,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -108,7 +102,7 @@

Author

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/portmanteau_tests.html b/dev/reference/portmanteau_tests.html index cd37ab2e..f358a7ce 100644 --- a/dev/reference/portmanteau_tests.html +++ b/dev/reference/portmanteau_tests.html @@ -2,13 +2,7 @@ Portmanteau tests — ljung_box • feasts +
@@ -23,7 +17,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -119,12 +113,12 @@

See also

Examples

ljung_box(rnorm(100))
-#>    lb_stat  lb_pvalue 
-#> 0.05699141 0.81131593 
+#>   lb_stat lb_pvalue 
+#> 0.2074180 0.6487987 
 
 box_pierce(rnorm(100))
-#>    bp_stat  bp_pvalue 
-#> 3.92697458 0.04751736 
+#>   bp_stat bp_pvalue 
+#> 0.5371549 0.4636142 
 
@@ -139,7 +133,7 @@

Examples

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/reexports.html b/dev/reference/reexports.html index 3eeb9edc..bda61332 100644 --- a/dev/reference/reexports.html +++ b/dev/reference/reexports.html @@ -17,13 +17,7 @@ "> +
@@ -38,7 +32,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -117,7 +111,7 @@

Objects exported from other packages

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/scale_cf_lag.html b/dev/reference/scale_cf_lag.html index 4a7011e6..49aa786c 100644 --- a/dev/reference/scale_cf_lag.html +++ b/dev/reference/scale_cf_lag.html @@ -5,13 +5,7 @@ This set of scales defines new scales for lagged time structures."> +
@@ -26,7 +20,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -111,7 +105,7 @@

Value

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/shift_level_max.html b/dev/reference/shift_level_max.html index 4278bed2..33539909 100644 --- a/dev/reference/shift_level_max.html +++ b/dev/reference/shift_level_max.html @@ -6,13 +6,7 @@ two consecutive windows."> +
@@ -27,7 +21,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -132,7 +126,7 @@

Author

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/stat_arch_lm.html b/dev/reference/stat_arch_lm.html index 960e19d4..377293d2 100644 --- a/dev/reference/stat_arch_lm.html +++ b/dev/reference/stat_arch_lm.html @@ -5,13 +5,7 @@ to \(x^2\)."> +
@@ -26,7 +20,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -122,7 +116,7 @@

Author

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/tile_features.html b/dev/reference/tile_features.html index 59329fa0..5bc4651c 100644 --- a/dev/reference/tile_features.html +++ b/dev/reference/tile_features.html @@ -4,13 +4,7 @@ the variance of the means, while lumpiness is the variance of the variances."> +
@@ -25,7 +19,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -123,7 +117,7 @@

Author

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/unitroot.html b/dev/reference/unitroot.html index af90f316..0f307379 100644 --- a/dev/reference/unitroot.html +++ b/dev/reference/unitroot.html @@ -2,13 +2,7 @@ Unit root tests — unitroot_kpss • feasts +
@@ -23,7 +17,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -138,7 +132,7 @@

See also

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.

diff --git a/dev/reference/unitroot_ndiffs.html b/dev/reference/unitroot_ndiffs.html index 321c1dba..09cb50dc 100644 --- a/dev/reference/unitroot_ndiffs.html +++ b/dev/reference/unitroot_ndiffs.html @@ -3,13 +3,7 @@ necessary to obtain a stationary time series."> +
@@ -24,7 +18,7 @@ feasts - 0.3.0.9000 + 0.3.2.9000
@@ -149,7 +143,7 @@

Details

-

Site built with pkgdown 2.0.6.

+

Site built with pkgdown 2.0.7.