Summary of Partial Channel Dependence with Channel Masks For Time Series Foundation Models, by Seunghan Lee et al.
Partial Channel Dependence with Channel Masks for Time Series Foundation Models
by Seunghan Lee, Taeyoung Park, Kibok Lee
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 The paper proposes a novel approach to addressing implicit heterogeneity in time series datasets, which has been overlooked in previous foundation model extensions. By introducing the concept of partial channel dependence (PCD), the authors aim to refine channel relationships using dataset-specific information. This is achieved through a channel mask that captures relative dependencies between channels and learns absolute dependencies specific to each dataset. The effectiveness of PCD is validated across four tasks, including forecasting, classification, imputation, and anomaly detection, under diverse settings with both TS foundation models and single-task models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to handle time series data by considering how different parts of the data relate to each other. This is important because different datasets can have different patterns or relationships between them. The authors propose a method called partial channel dependence (PCD) that takes these relationships into account. They show that this approach works well across different tasks and scenarios. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Mask » Time series
