Summary of Frequency-masked Embedding Inference: a Non-contrastive Approach For Time Series Representation Learning, by En Fu et al.
Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning
by En Fu, Yanyan Hu
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Frequency-masked Embedding Inference (FEI), a non-contrastive method that eliminates the need for positive and negative samples in self-supervised time series representation learning. FEI constructs two inference branches using frequency masking as prompts or the target series itself, enabling continuous semantic relationship modeling for time series data. The proposed approach outperforms existing contrast-based methods on 8 widely used time series datasets for classification and regression tasks, demonstrating improved generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to learn representations from time series data without needing positive and negative samples. It’s called Frequency-masked Embedding Inference (FEI) and it works by creating two paths of reasoning: one that uses the missing parts of the signal as prompts, and another that uses the whole signal as prompts. This allows FEI to capture the relationships between different frequencies in time series data, which is important for tasks like predicting stock prices or analyzing sensor readings. |
Keywords
» Artificial intelligence » Classification » Embedding » Generalization » Inference » Regression » Representation learning » Self supervised » Time series
