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nredell
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>.
Maintained by Nickalus Redell. Last updated 5 years ago.
deep-learningdirect-forecastingforecastforecastingmachine-learningmulti-step-ahead-forecastingneural-networkpythontime-series
130 stars 7.64 score 134 scripts
meghapsimatrix
simhelpers:Helper Functions for Simulation Studies
Calculates performance criteria measures and associated Monte Carlo standard errors for simulation results. Includes functions to help run simulation studies, following a general simulation workflow that closely aligns with the approach described by Morris, White, and Crowther (2019) <DOI:10.1002/sim.8086>. Also includes functions for calculating bootstrap confidence intervals (including normal, basic, studentized, percentile, bias-corrected, and bias-corrected-and-accelerated) with tidy output, as well as for extrapolating confidence interval coverage rates and hypothesis test rejection rates following techniques suggested by Boos and Zhang (2000) <DOI:10.1080/01621459.2000.10474226>.
Maintained by Megha Joshi. Last updated 3 months ago.
11 stars 7.07 score 40 scripts