tsfgrnn
Time Series Forecasting Using GRNN
A general regression neural network (GRNN) is a variant of a Radial Basis Function Network characterized by a fast single-pass learning. 'tsfgrnn' allows you to forecast time series using a GRNN model Francisco Martinez et al. (2019) <doi:10.1007/978-3-030-20521-8_17> and Francisco Martinez et al. (2022) <doi:10.1016/j.neucom.2021.12.028>. When the forecasting horizon is higher than 1, two multi-step ahead forecasting strategies can be used. The model built is autoregressive, that is, it is only based on the observations of the time series. You can consult and plot how the prediction was done. It is also possible to assess the forecasting accuracy of the model using rolling origin evaluation.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.5 |
rolling linux/jammy R-4.5 | tsfgrnn_1.0.5.tar.gz |
164.0 KiB |
1.0.5 |
rolling linux/noble R-4.5 | tsfgrnn_1.0.5.tar.gz |
165.4 KiB |
1.0.5 |
rolling source/ R- | tsfgrnn_1.0.5.tar.gz |
82.6 KiB |
1.0.5 |
latest linux/jammy R-4.5 | tsfgrnn_1.0.5.tar.gz |
164.0 KiB |
1.0.5 |
latest linux/noble R-4.5 | tsfgrnn_1.0.5.tar.gz |
165.4 KiB |
1.0.5 |
latest source/ R- | tsfgrnn_1.0.5.tar.gz |
82.6 KiB |
1.0.5 |
2026-04-26 source/ R- | tsfgrnn_1.0.5.tar.gz |
82.6 KiB |
1.0.5 |
2026-04-23 source/ R- | tsfgrnn_1.0.5.tar.gz |
82.6 KiB |
1.0.5 |
2026-04-09 windows/windows R-4.5 | tsfgrnn_1.0.5.zip |
485.4 KiB |
1.0.5 |
2025-04-20 source/ R- | tsfgrnn_1.0.5.tar.gz |
82.6 KiB |