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mitchelloharawild committed Sep 16, 2024
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5 changes: 4 additions & 1 deletion README.Rmd
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```

# feasts <a href='https://feasts.tidyverts.org'><img src='man/figures/logo.png' align="right" height="138.5" /></a>

<!-- badges: start -->
[![R build status](https://github.com/tidyverts/feasts/workflows/R-CMD-check/badge.svg)](https://github.com/tidyverts/feasts/actions?workflow=R-CMD-check)
[![Coverage status](https://codecov.io/gh/tidyverts/feasts/branch/master/graph/badge.svg)](https://app.codecov.io/gh/tidyverts/feasts?branch=master)
[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/feasts)](https://cran.r-project.org/package=feasts)
[![CRAN status](https://www.r-pkg.org/badges/version/feasts)](https://CRAN.R-project.org/package=feasts)
[![Lifecycle: maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)
<!-- badges: end -->

## Overview

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# feasts <a href='https://feasts.tidyverts.org'><img src='man/figures/logo.png' align="right" height="138.5" /></a>

<!-- badges: start -->

[![R build
status](https://github.com/tidyverts/feasts/workflows/R-CMD-check/badge.svg)](https://github.com/tidyverts/feasts/actions?workflow=R-CMD-check)
[![Coverage
status](https://codecov.io/gh/tidyverts/feasts/branch/master/graph/badge.svg)](https://app.codecov.io/gh/tidyverts/feasts?branch=master)
[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/feasts)](https://cran.r-project.org/package=feasts)
[![CRAN
status](https://www.r-pkg.org/badges/version/feasts)](https://CRAN.R-project.org/package=feasts)
[![Lifecycle:
maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)
<!-- badges: end -->

## Overview

Expand Down Expand Up @@ -90,8 +94,8 @@ A common task in time series analysis is decomposing a time series into
some simpler components. The feasts package supports two common time
series decomposition methods:

- Classical decomposition
- STL decomposition
- Classical decomposition
- STL decomposition

<!--
* X11 decomposition
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#> 8 STL(Beer ~ season(window = Inf)) 1957 Q4 320 264. 69.0 -12.7 251.
#> 9 STL(Beer ~ season(window = Inf)) 1958 Q1 272 266. 2.14 4.32 270.
#> 10 STL(Beer ~ season(window = Inf)) 1958 Q2 233 266. -42.6 9.72 276.
#> # … with 208 more rows
#> # i 208 more rows
```

``` r
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``` r
aus_retail %>%
features(Turnover, feat_stl)
#> # A tibble: 152 × 11
#> State Indus…¹ trend…² seaso…³ seaso…⁴ seaso…⁵ spiki…⁶ linea…⁷ curva…⁸ stl_e…⁹ stl_e…˟
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Australian Capit… Cafes,0.989 0.562 0 10 5.15e-5 227. 48.5 0.281 0.187
#> 2 Australian Capit… Cafes,0.993 0.629 0 10 9.73e-5 342. 77.8 0.320 0.218
#> 3 Australian Capit… Clothi… 0.991 0.923 9 11 4.23e-6 131. 17.4 0.262 0.152
#> 4 Australian Capit… Clothi… 0.993 0.957 9 11 1.29e-5 195. 19.3 0.262 0.193
#> 5 Australian Capit… Depart… 0.977 0.980 9 11 2.21e-5 130. -43.9 -0.254 0.119
#> 6 Australian Capit… Electr… 0.992 0.933 9 11 2.68e-5 233. -9.07 0.308 0.207
#> 7 Australian Capit… Food r… 0.999 0.890 9 11 2.24e-4 1264. 199. 0.0866 0.268
#> 8 Australian Capit… Footwe… 0.982 0.944 9 11 3.69e-6 64.0 1.95 0.152 0.176
#> 9 Australian Capit… Furnit… 0.981 0.687 9 1 4.09e-5 141. -21.6 0.200 0.0812
#> 10 Australian Capit… Hardwa… 0.992 0.900 9 4 1.32e-5 173. 45.1 0.102 0.0796
#> # … with 142 more rows, and abbreviated variable names ¹​Industry, ²​trend_strength,
#> # ³​seasonal_strength_year, ⁴​seasonal_peak_year, ⁵​seasonal_trough_year, ⁶​spikiness, ⁷​linearity,
#> # ⁸​curvature, ⁹​stl_e_acf1, ˟​stl_e_acf10
#> # A tibble: 152 x 11
#> State Industry trend_strength seasonal_strength_year seasonal_peak_year seasonal_trough_year
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Australia~ Cafes, ~ 0.989 0.562 0 10
#> 2 Australia~ Cafes, ~ 0.993 0.629 0 10
#> 3 Australia~ Clothin~ 0.991 0.923 9 11
#> 4 Australia~ Clothin~ 0.993 0.957 9 11
#> 5 Australia~ Departm~ 0.977 0.980 9 11
#> 6 Australia~ Electri~ 0.992 0.933 9 11
#> 7 Australia~ Food re~ 0.999 0.890 9 11
#> 8 Australia~ Footwea~ 0.982 0.944 9 11
#> 9 Australia~ Furnitu~ 0.981 0.687 9 1
#> 10 Australia~ Hardwar~ 0.992 0.900 9 4
#> # i 142 more rows
#> # i 5 more variables: spikiness <dbl>, linearity <dbl>, curvature <dbl>, stl_e_acf1 <dbl>,
#> # stl_e_acf10 <dbl>
```

This allows you to visualise the behaviour of many time series (where
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