Man pages for ha0ye/MATSS-forecasting
Analysis and Synthesis of Forecasting Ecological Time Series
| 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. |
