Summary of Rhiots: a Framework For Evaluating Hierarchical Time Series Forecasting Algorithms, by Luis Roque et al.
RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms
by Luis Roque, Carlos Soares, Luís Torgo
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel framework called Robustness of Hierarchically Organized Time Series (RHiOTS) is introduced to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets. The RHiOTS framework systematically alters existing datasets, simulating changes in data distribution through parameterizable transformations. It also incorporates innovative visualization components to turn complex results into interpretable visuals. This allows for an in-depth analysis of algorithm behavior under diverse conditions. The authors illustrate the use of RHiOTS by analyzing the predictive performance of several algorithms and found that traditional statistical methods are more robust than state-of-the-art deep learning algorithms, except when the transformation effect is highly disruptive. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to test how well forecasting models work in different situations. The model, called RHiOTS, takes real-world datasets and makes changes to them to see how different algorithms perform. It also helps make sense of the results by showing complex information in a clear way. This can help researchers choose the best algorithm for a specific problem. |
Keywords
» Artificial intelligence » Deep learning » Time series
