Package: seer 1.1.8
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>.
Authors:
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seer.pdf |seer.html✨
seer/json (API)
# Install 'seer' in R: |
install.packages('seer', repos = c('https://thiyangt.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/thiyangt/seer/issues
Last updated 2 years agofrom:abd4c2aa17. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 31 2024 |
R-4.5-win | OK | Oct 31 2024 |
R-4.5-linux | OK | Oct 31 2024 |
R-4.4-win | OK | Oct 31 2024 |
R-4.4-mac | OK | Oct 31 2024 |
R-4.3-win | OK | Oct 31 2024 |
R-4.3-mac | OK | Oct 31 2024 |
Exports:accuracy_arimaaccuracy_etsaccuracy_mstlaccuracy_nnaccuracy_rwaccuracy_rwdaccuracy_snaiveaccuracy_stlaraccuracy_tbatsaccuracy_thetaaccuracy_wnacf_seasonalDiffacf5build_rfcal_featurescal_m4measurescal_MASEcal_medianscaledcal_sMAPEcal_WAclassify_labelsclasslabelcombination_forecast_insideconvert_mstse_acf1fcast_accuracyfforms_combinationforecastfforms_ensembleholtWinter_parametersprepare_trainingsetrf_forecastsim_arimabasedsim_etsbasedsim_mstlbasedsplit_namesstlarunitroot
Dependencies:clicodetoolscolorspacecurldigestdplyrfansifarverforecastforecThetafracdifffurrrfuturegenericsggplot2globalsgluegtableisobandjsonlitelabelinglatticelifecyclelistenvlmtestmagrittrMASSMatrixmgcvmunsellnlmennetparallellypillarpkgconfigpurrrquadprogquantmodR6randomForestRColorBrewerRcppRcppArmadilloRcppRollrlangscalesstringistringrtibbletidyselecttimeDatetseriestsfeaturesTTRurcautf8vctrsviridisLitewithrxtszoo