seer: Feature-Based Forecast Model Selection

A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at <>.

Getting started

Package details

AuthorThiyanga Talagala [aut, cre] (<>), Rob J Hyndman [ths, aut] (<>), George Athanasopoulos [ths, aut]
MaintainerThiyanga Talagala <>
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the seer package in your browser

Any scripts or data that you put into this service are public.

seer documentation built on June 1, 2021, 9:07 a.m.