Time Series Forecasting Functions

Fundamental time series forecasting models such as autoregressive integrated moving average (ARIMA), exponential smoothing, and simple moving average are included. For ARIMA models, the output follows the traditional parameterisation by Box and Jenkins (1970, ISBN: 0816210942, 9780816210947). Furthermore, there are functions for detailed time series exploration and decomposition, respectively. All data and result visualisations are generated by 'ggplot2' instead of conventional R graphical output. For more details regarding the theoretical background of the models see Hyndman, R.J. and Athanasopoulos, G. (2021) < https://otexts.com/fpp3/>.


Reference manual

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install.packages("tsforecast")

1.3.1 by Ka Yui Karl Wu, 3 months ago


Browse source code at https://github.com/cran/tsforecast


Authors: Ka Yui Karl Wu [aut, cre]


Documentation:   PDF Manual  


GPL-3 license


Imports ggplot2, lubridate, forecast, tseries, scales

Suggests testthat


See at CRAN