Libra: A Benchmark for Time Series Forecasting Methods
Description
Libra, a forecasting benchmark, automatically evaluates forecasting methods based on their performance in a diverse set of evaluation scenarios. The benchmark comprises four different use cases, each covering 100 heterogeneous time series taken from different domains.
The GitHub page includes documentation, getting started, and examples.
Requirements
In order to use and install this R package, ensure that R (≥ 3.2) is installed.
Website
https://github.com/DescartesResearch/ForecastBenchmark
Contributors
- André Bauer
Maintainers
-
André Bauer, andre.bauer(at)uni-wuerzburg.de
University of Würzburg
Am Hubland, 97074 Würzburg, Germany
Version
1.0
License
GPLv3
Related publications and projects
- André Bauer, Marwin Züfle, Simon Eismann, Johannes Grohmann, Nikolas Herbst, and Samuel Kounev. 2021. "Libra: A Benchmark for Time Series Forecasting Methods". In Proceedings of the ACM/SPEC International Conference on Performance Engineering (ICPE '21). Association for Computing Machinery, New York, NY, USA, 189–200. doi: 10.1145/3427921.3450241
- Leznik, M., Iqbal, M. S., Trubin, I., Lochner, A., Jamshidi, P., & Bauer, A. 2022. "Change Point Detection for MongoDB Time Series Performance Regression". In Proceedings of the 11th ACM/SPEC International Conference on Performance Engineering.
- Meisenbacher, S., Turowski, M., Phipps, K., Rätz, M., Müller, D., Hagenmeyer, V., & Mikut, R. 2022. "Review of automated time series forecasting pipelines". Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, e1475.
- Godahewa, R., Bergmeir, C., Webb, G. I., Hyndman, R. J., & Montero-Manso, P. 2021. "Monash time series forecasting archive". arXiv preprint arXiv:2105.06643.