Package: iForecast 1.1.0
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Ho Tsung-wu
iForecast: Machine Learning Time Series Forecasting
Compute static, onestep and multistep time series forecasts for machine learning models.
Authors:
iForecast_1.1.0.tar.gz
iForecast_1.1.0.tar.gz(r-4.5-noble)iForecast_1.1.0.tar.gz(r-4.4-noble)
iForecast_1.1.0.tgz(r-4.4-emscripten)iForecast_1.1.0.tgz(r-4.3-emscripten)
iForecast.pdf |iForecast.html✨
iForecast/json (API)
# Install 'iForecast' in R: |
install.packages('iForecast', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 months agofrom:f1f91a2bc1. Checks:2 OK. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Feb 07 2025 |
R-4.5-linux | OK | Feb 07 2025 |
Exports:AccuracyiForecastrollingWindowstts.autoMLtts.caretttsAutoMLttsCaretttsLSTM
Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrpartscalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithrzoo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Accuracy measures for a forecast model | Accuracy |
Economic and Financial Data Sets | bc ES_15m ES_Daily macrodata |
Extract predictions and class probabilities from train objects | iForecast |
Defunct functions in package 'iForecast' | ttsAutoML |
Defunct functions in package 'iForecast' | ttsCaret |
Defunct functions in package 'iForecast' | ttsLSTM |
Rolling timeframe for time series anaysis | rollingWindows |
Train time series by automatic machine learning of 'h2o' provided by H2O.ai | tts.autoML |
Train time series by 'caret' and produce two types of time series forecasts: static and dynamic | tts.caret |