Summary of Can Time Series Forecasting Be Automated? a Benchmark and Analysis, by Anvitha Thirthapura Sreedhara et al.
Can time series forecasting be automated? A benchmark and analysis
by Anvitha Thirthapura Sreedhara, Joaquin Vanschoren
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning framework for evaluating and ranking time series forecasting methods is proposed, addressing the challenge of selecting suitable methods for given datasets. The framework compares performance across various datasets using prominent frameworks AutoGluon-Timeseries and sktime, shedding light on their applicability in real-world scenarios. This research contributes to time series forecasting by providing a robust benchmarking methodology, facilitating informed decision-making when choosing optimal prediction methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is important for many fields like finance, healthcare, and weather. Choosing the right method can be tricky because data patterns are different. Researchers created a way to compare and rank many forecasting methods across various datasets. They looked at two popular frameworks, AutoGluon-Timeseries and sktime, to see how well they work in real-world situations. This helps with choosing the best method for making good predictions. |
Keywords
» Artificial intelligence » Machine learning » Time series
