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/>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.3.0 |
rolling linux/jammy R-4.5 | tsforecast_1.3.0.tar.gz |
285.0 KiB |
1.3.0 |
rolling linux/noble R-4.5 | tsforecast_1.3.0.tar.gz |
284.6 KiB |
1.3.0 |
rolling source/ R- | tsforecast_1.3.0.tar.gz |
68.8 KiB |
1.3.0 |
latest linux/jammy R-4.5 | tsforecast_1.3.0.tar.gz |
285.0 KiB |
1.3.0 |
latest linux/noble R-4.5 | tsforecast_1.3.0.tar.gz |
284.6 KiB |
1.3.0 |
latest source/ R- | tsforecast_1.3.0.tar.gz |
68.8 KiB |
1.3.0 |
2026-04-26 source/ R- | tsforecast_1.3.0.tar.gz |
68.8 KiB |
1.3.0 |
2026-04-23 source/ R- | tsforecast_1.3.0.tar.gz |
68.8 KiB |
1.3.0 |
2026-04-09 windows/windows R-4.5 | tsforecast_1.3.0.zip |
296.3 KiB |