Summary of Utilizing Image Transforms and Diffusion Models For Generative Modeling Of Short and Long Time Series, by Ilan Naiman et al.
Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series
by Ilan Naiman, Nimrod Berman, Itai Pemper, Idan Arbiv, Gal Fadlon, Omri Azencot
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 generative model for time series data transforms sequences into images, allowing it to leverage advanced diffusion vision models and process both short- and long-range inputs within the same framework. The approach exploits invertible transforms such as delay embedding and short-time Fourier transform, unlocking three main advantages: using diffusion vision models, processing varying-length inputs, and harnessing tools from time series to image literature. The model is evaluated across multiple tasks, including unconditional generation, interpolation, and extrapolation, achieving state-of-the-art results with remarkable mean improvements of 58.17% in the short discriminative score and 132.61% in ultra-long classification scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a time series – like data from a sensor or a stock market graph – and turning it into an image. That’s what this new generative model does! It uses special tricks called invertible transforms to make the process work, which lets it handle short and long sequences in the same way. This is useful because most existing models are only good at one or the other. The team tested their idea on some big tasks like generating new data, filling gaps, and predicting what will happen next. They did really well, beating the current best models by a lot! |
Keywords
» Artificial intelligence » Classification » Diffusion » Embedding » Generative model » Time series
