Libra: A Benchmark for Time Series Forecasting Methods
| Autor: | Johannes Grohmann, Simon Eismann, Samuel Kounev, Andre Bauer, Marwin Züfle, Nikolas Herbst |
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| Rok vydání: | 2021 |
| Předmět: |
Series (mathematics)
business.industry Computer science 05 social sciences Benchmarking Machine learning computer.software_genre 01 natural sciences Task (project management) Data set Set (abstract data type) 010104 statistics & probability 0502 economics and business Benchmark (computing) Use case Artificial intelligence 0101 mathematics Time series business computer 050205 econometrics |
| Zdroj: | ICPE |
| Popis: | In many areas of decision making, forecasting is an essential pillar. Consequently, there are many different forecasting methods. According to the "No-Free-Lunch Theorem", there is no single forecasting method that performs best for all time series. In other words, each method has its advantages and disadvantages depending on the specific use case. Therefore, the choice of the forecasting method remains a mandatory expert task. However, expert knowledge cannot be fully automated. To establish a level playing field for evaluating the performance of time series forecasting methods in a broad setting, we propose Libra, a forecasting benchmark that automatically evaluates and ranks 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 data set was assembled from publicly available time series and was designed to exhibit much higher diversity than existing forecasting competitions. Based on this benchmark, we perform a comprehensive evaluation to compare different existing time series forecasting methods. |
| Databáze: | OpenAIRE |
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