Summary of Text2freq: Learning Series Patterns From Text Via Frequency Domain, by Ming-chih Lo et al.
Text2Freq: Learning Series Patterns from Text via Frequency Domain
by Ming-Chih Lo, Ching Chang, Wen-Chih Peng
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 model, Text2Freq, is a cross-modality approach that combines time series data and textual information to improve traditional time series forecasting. The existing models rely heavily on historical numeric values, overlooking the valuable insights provided by textual descriptions of special events. To address this modality gap, Text2Freq aligns textual information with the low-frequency components of time series data in the frequency domain. This allows for more effective and interpretable alignments between text and time series data. The model is evaluated on paired datasets of real-world stock prices and synthetic texts, achieving state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text2Freq is a new way to predict future events by combining two types of information: numbers about what happened in the past, and words that describe what happened. Right now, most models just use the numbers, but they don’t take into account things like special events or important announcements that can affect what happens in the future. The Text2Freq model tries to fix this problem by matching these events with the patterns in the data. It works really well and could be useful for predicting things like stock prices. |
Keywords
» Artificial intelligence » Time series
