Transformers in Time-series Analysis: A Tutorial
It serves as a resource for researchers and practitioners interested in applying Transformers to time-series data, but it is incremental as it compiles existing knowledge without new results.
This tutorial provides an overview of the Transformer architecture and its applications in time-series analysis, including explanations of core components and enhancements for tackling time-series tasks.
Transformer architecture has widespread applications, particularly in Natural Language Processing and computer vision. Recently Transformers have been employed in various aspects of time-series analysis. This tutorial provides an overview of the Transformer architecture, its applications, and a collection of examples from recent research papers in time-series analysis. We delve into an explanation of the core components of the Transformer, including the self-attention mechanism, positional encoding, multi-head, and encoder/decoder. Several enhancements to the initial, Transformer architecture are highlighted to tackle time-series tasks. The tutorial also provides best practices and techniques to overcome the challenge of effectively training Transformers for time-series analysis.