Summary of A Hybrid Loss Framework For Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors, by Ronghui Han et al.
A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors
by Ronghui Han, Duanyu Feng, Hongyu Du, Hao Wang
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed study investigates the impact of overall loss on existing time series methods with sequence decomposition, highlighting potential biases in model learning that can limit forecasting performance. The research proposes a hybrid loss framework combining global and component losses, allowing models to prioritize critical sub-series while maintaining low overall loss. This approach is evaluated on multiple datasets, demonstrating an average improvement of 0.5-2% over existing methods without modifying the model architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is crucial for various industries. The study explores how current time series methods decompose time series into multiple sub-series and apply different model architectures to predict future values. Researchers found that the overall loss may introduce bias in model learning, hindering the learning of critical sub-series and limiting performance. To address this, they propose a new framework combining global and component losses, allowing models to focus on important sub-series while keeping overall loss low. This approach is tested on different datasets and shows an average improvement over existing methods. |
Keywords
» Artificial intelligence » Time series
