NEWS.md
gg_season() not working with daily data showing seasonality > 1 week.gg_irf() for plotting impulse responses (typically obtained from using IRF() with fable models).cointegration_johansen() and cointegration_phillips_ouliaris() from urca.gg_season() not wrapping across facet_period argument correctly.Minor patch to resolve CRAN check issues with ggplot2 v3.5.0 breaking changes.
gg_season() breaks issue with ggplot2 v3.5.0gg_season() now allows seasonal period identifying labels to be nudged and repelled with the labels_repel, labels_left_nudge, and labels_right_nudge arguments.gg_season() behaviour of max_col has been restored, where colours aren’t used if the number of subseries to be coloured exceeds this value. The default has changed to Inf since this function now supports continuous colour guides. A new argument max_col_discrete has been added to control the threshold for showing discrete and continuous colour guides (#150).guerrero() method to maintain a consistent subseries length by removing the first few observations of needed. This more closely matches the described method, and the implementation in the forecast package.grid.draw() method for ensemble graphics (gg_tsdisplay() and gg_tsresiduals()). This allows use of ggsave() with these plots (#149).generate(<STL>) returning $.sim as a num [1:n(1d)] instead of num [1:72] (fable/#336).gg_season() incorrectly grouping some seasonal subseries.CCF() now matches stats::ccf() x and y arguments (#144).Minor release for compatibility with an upcoming ggplot2 release. This release contains a few bug fixes and improvements to existing functionality.
gg_tsresiduals() function now allows the type of plotted residual to be controlled via the type argument.STL() decompositions. For data with a single seasonal pattern, the window has changed from 13 to 11. This change is based on results from simulation experiments.seasonal::seas() defaults were not being used in X_13ARIMA_SEATS() when defaults = "seasonal" (#130).gg_subseries() on data with spaces in the index column name (#136).Small patch to fix check issues on Solaris, and to resolve components() for automatically selected transformations in X_13ARIMA_SEATS().
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".X_13ARIMA_SEATS() method officially deprecates (supersedes) the X11() and SEATS() models which were previously not exported (#66).generate() method for STL() decompositions. The method uses a block bootstrap method to sample from the residuals.fitted() and residuals() methods for STL() decompositions.guerrero() default lower bound for Box-Cox lambda selection to from -1 to -0.9. A transformation parameter of -1 typically results from data which should not be transformed with a Box-Cox transformation, and can result in very inaccurate forecasts if such a strong and inappropriate transformation is used.A minor release to fix check issues introduced by changes in an upstream dependency.
gg_season() labels are low aligned outward (#115).gg_season() and gg_subseries() (#117).gg_season()
gg_lag() facets are now displayed with a 1:1 aspect ratio.n_flat_spots() function has been renamed to longest_flat_spot() to more accurately describe the feature.gg_season() and ggsubseries() date structure improvements.n_flat_spots() return name is now “longest_flat_spot” to better describe the feature.gg_tsdisplay() erroring when the spec.ar order is chosen to be 0.CCF() lag being spaced by multiples of the data’s frequency.gg_season() and gg_subseries() (#107).View() not working on ACF(), PACF() and CCF() outputs.Minor patch to resolve upstream check issues introduced by dplyr v1.0.0 and tsibble v0.9.0.
polar = TRUE in gg_season().ACF().feat_spectral() to use stats::spec.ar() instead of ForeCA::spectral_entropy(). Note that the feature value will be slightly different due to use of a different spectral estimator, and the fix of a bug in ForeCA.feat_stl().gg_lag() have been reversed for consistency with stats::lag.plot().feat_intermittent()
gg_tsdisplay() not working with plotting expressions of data.gg_subseries() erroring when certain column names are used (#95).STL() specials.var_tiled_var() no longer includes partial tile windows in the computation.feat_stl().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.feat_spectral() not showing results.ACF(), PACF() and CCF() for tidyr change.gg_tsdisplay() will no longer fail on non-seasonal data with missing values. The last plot will instead show a PACF in this case (#76)stat_arch_lm() (#85)gg_season, gg_subseries, gg_lag, gg_tsdisplay, gg_tsresiduals, gg_arma.ACF, PACF, CCF, and autoplot.tbl_cf
fabletools::features(): feat_stl, feat_acf, feat_pacf, guerrero, unitroot_kpss, unitroot_pp, unitroot_ndiffs, unitroot_nsdiffs, box_pierce, ljung_box, var_tiled_var, var_tiled_mean, shift_level_max, shift_var_max, shift_kl_max, feat_spectral, n_crossing_points, n_flat_spots, coef_hurst, stat_arch_lm
classical_decomposition, STL