Package index
-
backtest() - Choose a model by what would have worked
-
total_forecast() - Forecast of a total
-
autoplot(<foresight_backtest>)plot(<foresight_backtest>)autoplot(<foresight_decomposition>)plot(<foresight_decomposition>) - Charts of a backtest or a decomposition
-
theme_foresight() - The theme of the package's charts
-
candidates_default()candidates_thorough() - Ready sets of candidates
-
foresight_threads() - Threads used by backtests and ensembles
-
fit_model()predict(<foresight_fit>)forecast_model() - Fit a model and forecast
-
model_arima()model_airline() - Seasonal ARIMA
-
model_auto_arima() - ARIMA with automatic orders
-
model_box_cox()model_log() - A model on the log or Box-Cox scale
-
model_croston() - Intermittent demand
-
model_decomposed() - Forecast the seasonally adjusted series
-
model_ensemble() - Several models combined
-
model_ets()model_auto_ets() - Exponential smoothing
-
model_holt_winters() - Holt-Winters
-
model_log_linear() - Log-linear regression
-
model_mean()model_naive()model_drift()model_seasonal_naive() - Benchmark models
-
model_prophet() - Prophet-style trend with changepoints and events
-
model_tbats() - TBATS
-
model_theta() - Theta method
-
model_box_cox()model_log() - A model on the log or Box-Cox scale
-
model_decomposed() - Forecast the seasonally adjusted series
-
model_ensemble() - Several models combined
-
with_name() - Rename a model
-
model_name()model_description() - Name and description of a model
-
decompose_stl()decompose_mstl() - Decompose a series by STL or MSTL
-
fill_gaps()find_outliers()clean_series() - Gaps and outliers
-
kpss_statistic()n_differences()n_seasonal_differences()seasonal_strength()autocorrelations()box_cox()inv_box_cox()guerrero_lambda() - Tests and transformations
-
mape()pct_bias()mae()rmse()mase() - Accuracy of forecasts
-
fourier_terms()seasonal_dummies() - External variables for a regression with ARIMA errors
-
foresightrforesightr-package - foresightr: Forecasts Chosen by What Would Have Worked