transformers in time series

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Quick description about transformers in time series:

Transformers in Time Series is a curated research repository that collects academic papers, code implementations, datasets, and learning resources related to transformer models for time series analysis. The project was created to systematically organize the rapidly growing research field that applies transformer architectures to time series modeling tasks. It compiles literature from major conferences and journals and categorizes them by application domains such as forecasting, anomaly detection, and classification. The repository also provides a taxonomy that helps researchers understand different architectural variations of transformers designed for time series data. These models are particularly important because transformers can capture long-range dependencies in sequential data, which makes them well suited for complex temporal patterns in real-world datasets.

Features:

• Curated collection of research papers on transformers for time series modeling
• Organization of literature by task such as forecasting or anomaly detection
• Taxonomy describing architectural variations of time series transformers
• Links to code implementations and datasets for research experiments
• Reference materials related to a comprehensive academic survey
• Continuously updated repository tracking new developments in the field

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