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. |

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