DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting

Area of Science:

• Machine Learning
• Time Series Analysis
• Deep Learning

Background:

• Multivariate time series forecasting is vital for real-world applications.
• Current methods often use unified models, potentially missing channel-specific patterns.
• Channel-independent assumptions can limit prediction robustness.

Purpose of the Study:

• To propose DisenTS, a framework for disentangled channel evolving patterns in multivariate time series forecasting.
• To address limitations of unified models in capturing diverse channel characteristics like seasonality and trends.
• To improve forecasting accuracy by modeling distinct patterns in a decoupled manner.

Main Methods:

• Employs multiple distinct forecasting models, each targeting a unique evolving pattern.
• Introduces a Forecaster Aware Gate (FAG) for adaptive routing signals without supervised partition.
• Utilizes Linear Weight Approximation (LWA) for forecaster states and Similarity Constraint (SC) to specialize models.

Main Results:

• Extensive experiments on diverse forecasting settings demonstrate DisenTS effectiveness.
• The framework shows generalizability across various state-of-the-art forecasting models.
• DisenTS successfully models disentangled channel evolving patterns.

Conclusions:

• DisenTS offers a novel approach to multivariate time series forecasting by disentangling channel patterns.
• The proposed methods (FAG, LWA, SC) effectively guide the learning of specialized forecasting models.
• DisenTS framework enhances forecasting accuracy and generalizability for complex time series data.