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>.
|Author||Thiyanga Talagala [aut, cre] (<https://orcid.org/0000-0002-0656-9789>), Rob J Hyndman [ths, aut] (<https://orcid.org/0000-0002-2140-5352>), George Athanasopoulos [ths, aut]|
|Maintainer||Thiyanga Talagala <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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