Summary of Time Weaver: a Conditional Time Series Generation Model, by Sai Shankar Narasimhan et al.
Time Weaver: A Conditional Time Series Generation Model
by Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, Sandeep Chinchali
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 authors introduce Time Weaver, a novel diffusion-based model that generates time series data with paired heterogeneous contextual metadata. This approach improves upon existing conditional generation methods from image, audio, and video domains by leveraging categorical, continuous, and time-variant variables. The model is evaluated using a novel metric that captures the specificity of generated time series in reproducing metadata-specific features, outperforming state-of-the-art benchmarks like Generative Adversarial Networks (GANs) by up to 27% on real-world energy, medical, air quality, and traffic data sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine generating electricity demand patterns based on weather and location. This paper introduces Time Weaver, a new model that uses information about the weather, location, and other factors to create more realistic time series predictions. Current methods for predicting time series ignore this extra information, making it hard to adapt existing techniques from other domains like images or audio. Time Weaver is designed to handle these challenges by using diffusion-based modeling and a novel evaluation metric that checks how well the generated data matches the original metadata. The results show that Time Weaver outperforms previous methods on real-world datasets for energy, medical, air quality, and traffic forecasting. |
Keywords
* Artificial intelligence * Diffusion * Time series
