MSDformer: Multi-Scale Discrete Transformer for Time Series Generation.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Time Series Analysis

Background:

• Discrete Token Modeling (DTM) excels in non-natural language tasks like time series generation.
• Existing DTM methods struggle with multi-scale temporal patterns and lack theoretical guidance.
• Prior work SDformer achieved state-of-the-art but had limitations.

Purpose of the Study:

• To propose a novel multi-scale DTM-based method for time series generation.
• To address limitations in capturing multi-scale temporal patterns and provide theoretical foundations.
• To enhance the quality and complexity of generated time series data.

Main Methods:

• Developed Multi-Scale Discrete Transformer (MSDformer).
• Employed a multi-scale time series tokenizer for learning discrete tokens at various scales.
• Utilized multi-scale autoregressive token modeling within a discrete latent space.

Main Results:

• MSDformer significantly outperforms existing state-of-the-art methods in time series generation.
• Theoretical validation using the rate-distortion theorem supports the model's effectiveness.
• Demonstrated substantial enhancement in generated time series quality by incorporating multi-scale information.

Conclusions:

• MSDformer effectively captures multi-scale temporal patterns crucial for complex time series.
• The integration of multi-scale information and modeling enhances DTM-based time series generation.
• The proposed method offers a theoretically grounded and experimentally validated advancement in the field.