seer
Feature-Based Forecast Model Selection
A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1.8 |
rolling linux/jammy R-4.5 | seer_1.1.8.tar.gz |
131.0 KiB |
1.1.8 |
rolling linux/noble R-4.5 | seer_1.1.8.tar.gz |
131.2 KiB |
1.1.8 |
rolling source/ R- | seer_1.1.8.tar.gz |
32.5 KiB |
1.1.8 |
latest linux/jammy R-4.5 | seer_1.1.8.tar.gz |
131.0 KiB |
1.1.8 |
latest linux/noble R-4.5 | seer_1.1.8.tar.gz |
131.2 KiB |
1.1.8 |
latest source/ R- | seer_1.1.8.tar.gz |
32.5 KiB |
1.1.8 |
2026-04-26 source/ R- | seer_1.1.8.tar.gz |
32.5 KiB |
1.1.8 |
2026-04-23 source/ R- | seer_1.1.8.tar.gz |
32.5 KiB |
1.1.8 |
2026-04-09 windows/windows R-4.5 | seer_1.1.8.zip |
134.2 KiB |
1.1.8 |
2025-04-20 source/ R- | seer_1.1.8.tar.gz |
32.5 KiB |