{ "_id": "6acf618fe2d863f6629516df", "Package": "tsforecast", "Type": "Package", "Title": "Time Series Forecasting Functions", "Version": "1.3.1", "Date": "2026-08-01", "Authors@R": "c(\nperson(\n\"Ka Yui Karl\", \"Wu\",\nemail = \"karlwuky@suss.edu.sg\",\nrole = c(\"aut\", \"cre\")\n)\n)", "Maintainer": "Ka Yui Karl Wu ", "Language": "en-GB", "Description": "Fundamental time series forecasting models such as\nautoregressive integrated moving average (ARIMA), exponential\nsmoothing, and simple moving average are included. For ARIMA\nmodels, the output follows the traditional parameterisation by\nBox and Jenkins (1970, ISBN: 0816210942, 9780816210947).\nFurthermore, there are functions for detailed time series\nexploration and decomposition, respectively. All data and\nresult visualisations are generated by 'ggplot2' instead of\nconventional R graphical output. For more details regarding the\ntheoretical background of the models see Hyndman, R.J. and\nAthanasopoulos, G. 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