Summary of Modeling Temporal Dependencies Within the Target For Long-term Time Series Forecasting, by Qi Xiong et al.
Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting
by Qi Xiong, Kai Tang, Minbo Ma, Ji Zhang, Jie Xu, Tianrui Li
First submitted to arxiv on: 7 Jun 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 This paper addresses a critical issue in long-term time series forecasting (LTSF) methods, which struggle with modeling Temporal Dependencies within the Target (TDT). The proposed framework, Temporal Dependency Alignment (TDAlign), enhances existing LTSF methods by learning TDT patterns. TDAlign introduces a novel loss function that aligns prediction changes with target variations and an adaptive balancing strategy to integrate with existing methods. The plug-and-play framework adds minimal computational overhead and is tested on six strong LTSF baselines across seven real-world datasets, demonstrating significant performance improvements (average error reduction of 1.47-9.19% for predictions and 4.57-15.78% for change values). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make better predictions about what will happen in the future by fixing a problem with current prediction methods. The issue is that these methods don’t understand how things change over time. The new approach, called Temporal Dependency Alignment (TDAlign), teaches existing prediction models to recognize patterns of change and use them to improve their forecasts. TDAlign works well with many different prediction methods and datasets, and it makes predictions more accurate by reducing errors by an average of 1-9%. |
Keywords
» Artificial intelligence » Alignment » Loss function » Time series
