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).... in ACF(), PACF(), and CCF() with y (and x for CCF()) arguments. This change should not affect the code for most users, but is important for the eventual passing of ... to acf(), pacf() and ccf() in a future version (#124).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_cffabletools::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_lmclassical_decomposition, STL