Summary of Explanation Space: a New Perspective Into Time Series Interpretability, by Shahbaz Rezaei and Xin Liu
Explanation Space: A New Perspective into Time Series Interpretability
by Shahbaz Rezaei, Xin Liu
First submitted to arxiv on: 2 Sep 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 This paper proposes a new method for explaining deep learning models in time series data, which is crucial for many applications where users need to understand the importance of each input feature. Unlike image or tabular data, time series features are often difficult to manifest in the time domain, making it hard for users to understand their impact on the model’s decisions. The authors suggest that existing explanation methods from tabular and vision domains may not be directly applicable to time series data due to differences in how features are defined. To address this issue, they propose a simple yet effective method that allows models trained on time domain data to be interpreted using existing XAI methods. This method can be easily adopted without requiring changes to the trained models or XAI methods. The proposed approach includes four explanation spaces that can help alleviate issues in different types of time series data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make deep learning models more understandable for people working with time series data. Right now, it’s hard to explain why a model made a certain decision because the features are difficult to understand. The authors want to fix this by creating a new way to interpret these models using existing methods. They think that current methods might not work well with time series data because the features aren’t defined in the same way as image or table data. To solve this problem, they came up with a simple and effective method that allows models trained on time series data to be explained using existing techniques. |
Keywords
» Artificial intelligence » Deep learning » Time series
