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 < https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.


Reference manual

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1.1.6 by Thiyanga Talagala, 5 months ago

Browse source code at https://github.com/cran/seer

Authors: Thiyanga Talagala [aut, cre] , Rob J Hyndman [ths, aut] , George Athanasopoulos [ths, aut]

Documentation:   PDF Manual  

Task views: Time Series Analysis

GPL-3 license

Imports stats, urca, forecast, dplyr, magrittr, randomForest, forecTheta, stringr, tibble, purrr, future, furrr, utils, tsfeatures, MASS

Suggests testthat, covr, repmis, knitr, rmarkdown, ggplot2, tidyr, Mcomp, GGally

See at CRAN