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thiyangt
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 <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.
Maintained by Thiyanga Talagala. Last updated 2 years ago.
78 stars 5.31 score 52 scripts
rjdverse
rjd3revisions:Revision analysis with 'JDemetra+ 3.x'
Revision analysis tool part of 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It performs a battery of tests on revisions and submit a report with the results. The various tests enable the users to detect potential bias and sources of inefficiency in preliminary estimates.
Maintained by Corentin Lemasson. Last updated 10 days ago.
3 stars 5.01 score 4 scripts