Summary of Self-supervised Learning Of Time Series Representation Via Diffusion Process and Imputation-interpolation-forecasting Mask, by Zineb Senane et al.
Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask
by Zineb Senane, Lele Cao, Valentin Leonhard Buchner, Yusuke Tashiro, Lei You, Pawel Herman, Mats Nordahl, Ruibo Tu, Vilhelm von Ehrenheim
First submitted to arxiv on: 9 May 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 Time Series Representation Learning (TSRL) is a machine learning subfield focused on generating informative representations for various time series modeling tasks. Traditional Self-Supervised Learning (SSL) methods in TSRL, including reconstructive, adversarial, contrastive, and predictive approaches, struggle with sensitivity to noise and data nuances. Recent diffusion-based methods have shown advanced generative capabilities but primarily target specific application scenarios like imputation and forecasting. Our work, Time Series Diffusion Embedding (TSDE), addresses this gap by introducing the first diffusion-based SSL TSRL approach. TSDE segments time series data into observed and masked parts using an Imputation-Interpolation-Forecasting (IIF) mask. A trainable embedding function featuring dual-orthogonal Transformer encoders with a crossover mechanism is applied to the observed part, followed by training a reverse diffusion process conditioned on the embeddings designed to predict noise added to the masked part. Extensive experiments demonstrate TSDE’s superiority in imputation, interpolation, forecasting, anomaly detection, classification, and clustering. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time Series Representation Learning (TSRL) is about teaching computers to understand time series data better. Currently, there are four main ways to do this: reconstructive, adversarial, contrastive, and predictive. However, these methods struggle with noisy or complex data. Recently, a new approach called diffusion-based SSL has shown promise in generating accurate representations of time series data. Our research, Time Series Diffusion Embedding (TSDE), is the first to apply this approach to general time series modeling tasks. We segment the data into parts we know and don’t know, then use a special algorithm to learn how to predict what’s missing. This allows us to improve performance in various tasks such as filling gaps in the data, predicting future values, detecting unusual patterns, classifying data, and grouping similar data together. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Clustering » Diffusion » Embedding » Machine learning » Mask » Representation learning » Self supervised » Time series » Transformer
