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tsforecast  

Time Series Forecasting Functions
View on CRAN: Click here


Download and install tsforecast package within the R console
Install from CRAN:
install.packages("tsforecast")

Install from Github:
library("remotes")
install_github("cran/tsforecast")

Install by package version:
library("remotes")
install_version("tsforecast", "1.3.0")



Attach the package and use:
library("tsforecast")
Maintained by
Ka Yui Karl Wu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-12-15
Latest Update: 2025-12-15
Description:
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/>.
How to cite:
Ka Yui Karl Wu (2025). tsforecast: Time Series Forecasting Functions. R package version 1.3.0, https://cran.r-project.org/web/packages/tsforecast. Accessed 12 Sep. 2026.
Previous versions and publish date:
(2026-08-01 19:00), 1.1.0 (2025-12-12 22:20), 1.2.0 (2025-12-15 09:20), 1.2.1 (2025-12-19 14:50), 1.3.0 (2026-01-21 13:40)
Other packages that cited tsforecast R package
View tsforecast citation profile
Other R packages that tsforecast depends, imports, suggests or enhances
Complete documentation for tsforecast
Functions, R codes and Examples using the tsforecast R package
Full tsforecast package functions and examples
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