Summary of Hierarchical Neural Additive Models For Interpretable Demand Forecasts, by Leif Feddersen et al.
Hierarchical Neural Additive Models for Interpretable Demand Forecasts
by Leif Feddersen, Catherine Cleophas
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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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 In this paper, researchers tackle the challenge of making machine learning models more interpretable and acceptable in demand forecasting. They propose Hierarchical Neural Additive Models for Time Series (HNAM), an approach that builds upon Neural Additive Models (NAM) to better handle time-series data. HNAM incorporates a novel additive model with level and interacting covariate components, aiming to improve the accuracy of forecasts while making them more understandable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Demand forecasting is crucial for business decisions, but traditional machine learning models often lack interpretability and acceptance. To address this issue, researchers introduce Hierarchical Neural Additive Models for Time Series (HNAM), which combines level and interacting covariate components. This approach aims to improve forecast accuracy while making it more understandable. |
Keywords
* Artificial intelligence * Machine learning * Time series
