Package: forecastML 0.9.1

forecastML: Time Series Forecasting with Machine Learning Methods

The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.

Authors:Nickalus Redell

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Bug tracker:https://github.com/nredell/forecastml/issues

On CRAN:

deep-learningdirect-forecastingforecastforecastingmachine-learningmulti-step-ahead-forecastingneural-networkpythontime-series

11 exports 130 stars 4.56 score 47 dependencies 127 scripts 364 downloads

Last updated 4 years agofrom:282ebe0b7e. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:calculate_intervalscombine_forecastscreate_lagged_dfcreate_skeletoncreate_windowsfill_gapsreconcile_forecastsresidualsreturn_errorreturn_hypertrain_model

Dependencies:clicodetoolscolorspacecpp11data.tabledigestdplyrdtplyrfansifarverfuturefuture.applygenericsggplot2globalsgluegtableisobandlabelinglatticelifecyclelistenvlubridatemagrittrMASSMatrixmgcvmunsellnlmeparallellypillarpkgconfigpurrrR6RColorBrewerrlangscalesstringistringrtibbletidyrtidyselecttimechangeutf8vctrsviridisLitewithr