EQTPartners/TSDE
TSDE is a novel SSL framework for TSRL, the first of its kind, effectively harnessing a diffusion process, conditioned on an innovative dual-orthogonal Transformer encoder architecture with a crossover mechanism, and employing a unique IIF mask strategy (KDD 2024, main research track).
This project provides advanced self-supervised learning for time series data, enabling accurate predictions and insights even with incomplete information. It takes raw time series data (like sensor readings, stock prices, or patient vitals) and can fill in missing gaps, forecast future values, detect anomalies, or group similar patterns. Data scientists and machine learning engineers working with complex sequential datasets can use this for enhanced time series analysis.
No commits in the last 6 months.
Use this if you need to perform imputation, interpolation, forecasting, anomaly detection, classification, or clustering on time series data with state-of-the-art accuracy, especially when dealing with missing data points.
Not ideal if you are a business user looking for a no-code solution or someone without a strong background in machine learning and Python programming.
Stars
32
Forks
8
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Aug 13, 2024
Commits (30d)
0
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