initialize_params_naive: Initialize paramaters to optimize over based on a set of...

In timradtke/heuristika: Robust Probabilistic Forecasts to Tinker With

Initialize paramaters to optimize over based on a set of naive models

Description

Use this function to generate a parameter grid (matrix) that can be provided to the param_grid argument of tulip(). The model estimation then reduces to evaluating all provided combinations and choosing the best one. See Details for more. Note that there is nothing special about the matrix generated by this function—you can define a set of possible parameters in any way that suits you.

Usage

initialize_params_naive()

Value

A numeric matrix with six named columns: 'alpha', 'one_minus_alpha', 'beta', 'one_minus_beta', 'gamma', 'one_minus_gamma'. The alpha paramaters belong to the model's level component, the beta parameters to the model's trend component, and the gamma parameters to the model's seasonality component. Each pair usually adds up to 1, however dampening effectively reduces the sum of beta and one_minus_beta to less than 1. As per assertions on tulip()'s param_grid, each row must sum up to a value between 0 and 3, the columns must be named and in order, and each individual value must be between 0 and 1.

References

Rob J. Hyndman, Anne B. Koehler, Ralph D. Snyder, and Simone Grose (2002). A State Space Framework for Automatic Forecasting using Exponential Smoothing Methods.

https://doi.org/10.1016/S0169-2070(01)00110-8

See Also

tulip(), initialize_params_random(), initialize_params_grid()

Examples

initialize_params_naive()

timradtke/heuristika documentation built on April 24, 2023, 1:55 a.m.