Summary of From Pixels to Predictions: Spectrogram and Vision Transformer For Better Time Series Forecasting, by Zhen Zeng et al.
From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecasting
by Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa, Tucker Balch, Manuela Veloso
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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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 approach uses time-frequency spectrograms as the visual representation of time series data, leveraging a vision transformer for multimodal learning. This novel method showcases advantages across diverse datasets from different domains, outperforming statistical baselines (EMA and ARIMA), state-of-the-art deep learning-based approaches (DeepAR), and other visual representations of time series data (lineplot images). An ablation study demonstrates the benefits of utilizing spectrograms as a visual representation for time series data. The vision transformer’s simultaneous learning in both time and frequency domains enhances the approach’s effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is important for making good decisions, but it can be tricky. Some people are trying to use computer vision models to help with this problem. They’re using special graphs called lineplots to show how the data changes over time. But researchers have come up with a new idea: instead of lineplots, they’re using something called time-frequency spectrograms to represent time series data. This new method uses a special kind of model called a vision transformer that can learn from both the time and frequency domains at the same time. The results show that this approach is better than some other methods for forecasting. |
Keywords
» Artificial intelligence » Deep learning » Time series » Vision transformer
