Summary of Mff-ftnet: Multi-scale Feature Fusion Across Frequency and Temporal Domains For Time Series Forecasting, by Yangyang Shi et al.
MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting
by Yangyang Shi, Qianqian Ren, Yong Liu, Jianguo Sun
First submitted to arxiv on: 26 Nov 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 introduces MFF-FTNet, a novel framework for time series forecasting that addresses the challenges of noise, data sparsity, and capturing complex multi-scale patterns. The architecture combines contrastive learning with multi-scale feature extraction across both frequency and time domains. An adaptive noise augmentation strategy adjusts scaling and shifting factors based on statistical properties to enhance model resilience. The framework consists of two modules: Frequency-Aware Contrastive Module (FACM) and Complementary Time Domain Contrastive Module (CTCM). A unified feature representation strategy enables robust contrastive learning, creating an enriched framework for accurate forecasting. Experimental results on five real-world datasets show that MFF-FTNet outperforms state-of-the-art models by 7.7% in multivariate tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in predicting what will happen next in a series of numbers. Right now, computers struggle to make accurate predictions because the data can be noisy and tricky to understand. The new approach combines two techniques: learning patterns across different scales and adjusting for noise in the data. This helps the computer learn more effectively from the data and make better predictions. The results show that this new method is much better than existing methods at predicting what will happen next, which is important in many fields like finance and weather forecasting. |
Keywords
» Artificial intelligence » Feature extraction » Time series
