tsforecast: 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/>.

Version:	1.1.0
Depends:	R (≥ 3.5.0)
Imports:	ggplot2, lubridate, forecast, tseries, scales
Suggests:	testthat (≥ 3.0.0)
Published:	2025-12-12
DOI:	10.32614/CRAN.package.tsforecast (may not be active yet)
Author:	Ka Yui Karl Wu [aut, cre]
Maintainer:	Ka Yui Karl Wu <karlwuky at suss.edu.sg>
License:	GPL-3
NeedsCompilation:	no
Language:	en-GB
CRAN checks:	tsforecast results

Documentation:

Reference manual:

tsforecast.html , tsforecast.pdf

Downloads:

Package source:	tsforecast_1.1.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): tsforecast_1.1.0.tgz

Linking:

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