forecastsuite: Multi-Model Time Series Forecasting Toolkit and Local App

Fits, compares and visualizes univariate time series forecasts through an extensible model registry built largely on the 'forecast' package: AutoRegressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) models (Hyndman and Khandakar (2008) <doi:10.18637/jss.v027.i03>), Error, Trend, Seasonal (ETS) exponential smoothing (Hyndman et al. (2002) <doi:10.1016/S0169-2070(01)00110-8>), Trigonometric seasonality, Box-Cox transformation, AutoRegressive Moving Average (ARMA) errors, Trend and Seasonal components (TBATS) models (De Livera, Hyndman and Snyder (2011) <doi:10.1198/jasa.2011.tm09771>), Neural Network AutoRegression (NNETAR) and Holt-Winters exponential smoothing (Winters (1960) <doi:10.1287/mnsc.6.3.324>), plus optional 'Prophet' models via the 'prophet' package (Taylor and Letham (2018) <doi:10.1080/00031305.2017.1380080>) and Long Short-Term Memory (LSTM) networks via the 'torch' package (Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>). Also provides grouped series with bottom-up and top-down reconciliation, rolling-origin cross-validation, ensembles and residual diagnostics, following Hyndman and Athanasopoulos (2021) <https://otexts.com/fpp3/>, and a bundled local 'shiny' application exposing every model.

install.packages("forecastsuite")

Try the forecastsuite package in your browser