arima_ts | Make forecasts using AR / ARIMA |
build_methods_plan | Make a drake plan with forecasting methods to be compared |
build_ward_data_plan | Make a drake plan with all the datasets in Ward et al. 2014 |
build_ward_methods_plan | Make a drake plan with all the forecasting methods in Ward et... |
check_time_series | Check time series for potentially problematic features |
compute_subset_range | Compute the indices corresponding to a defined subset |
embedd | Embed a time series |
ets_ts | Exponentially smoothed time series model |
expect_forecasts | Check if object is in valid forecasts format |
expect_NA_warnings | Check if warnings are expected for a too-short time series |
forecast_iterated | Iterated one-step forecasting (no refitting) |
gam_ts | Make forecasts using a Generalized Additive Model |
gausspr_ts | Make forecasts using gaussian process regression |
get_LPI_data | Read in the LPI time series |
get_ward_data | Read in a specific database from Ward et al. 2014 |
hindcast | Hindcasting with one-step forecasts |
lm_ts | Make forecasts using a Linear Model |
locreg_ts | Make forecasts using locally weighted regression |
marss_ts | Make forecasts using a state space model |
naive_one_step | Naive one-step ahead forecast |
nnet_ts | Make forecasts using neural network time series model |
npreg_ts | Make forecasts using nonparametric kernel regression |
PE | Calculate the permuation entropy of a times series |
pipe | Pipe operator |
randomwalk_ts | Forecast using a random walk model |
ranfor_ts | Make forecasts using a random forest model |
reshape_ward_data | Reshape the processed data file from Ward et al. 2014 |
simplex_ts | Make forecasts using simplex projection or S-maps |
sts_ts | Structural time series model |
word_distribution | Compute the distribution of "word"s in a time series. |
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