Summary of Adaptive Multi-scale Decomposition Framework For Time Series Forecasting, by Yifan Hu et al.
Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting
by Yifan Hu, Peiyuan Liu, Peng Zhu, Dawei Cheng, Tao Dai
First submitted to arxiv on: 6 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 The proposed Adaptive Multi-Scale Decomposition (AMD) framework leverages MLP-based methods to address the challenges of time series forecasting. By decomposing time series into distinct temporal patterns at multiple scales, AMD effectively captures complex temporal patterns and models both temporal and channel dependencies. The Dual Dependency Interaction block and Adaptive Multi-predictor Synthesis block complement MDM’s residual decomposition, allowing for refined multi-scale data integration through autocorrelation. Experimental results demonstrate state-of-the-art performance in both long-term and short-term forecasting tasks across various datasets, showcasing superior efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The AMD framework is a new way to forecast time series data using machine learning. It breaks down the data into smaller pieces at different scales, which helps it understand complex patterns. The framework uses two special blocks: one that combines information from different scales and another that predicts missing values. This makes it good at both short-term and long-term predictions. The results show that AMD works better than other methods for forecasting time series data. |
Keywords
» Artificial intelligence » Machine learning » Time series
