Adapformer: Adaptive channel management for multivariate time series forecasting

Area of Science:

• Artificial Intelligence
• Machine Learning
• Time Series Analysis

Background:

• Multivariate time series forecasting (MTSF) faces challenges in modeling inter-variable dependencies.
• Existing channel-independent (CI) and channel-dependent (CD) methods have limitations, either ignoring interactions or introducing noise.
• There is a need for advanced MTSF models that balance capturing dependencies with predictive efficiency.

Purpose of the Study:

• To introduce the Adaptive Forecasting Transformer (Adapformer), a novel framework for MTSF.
• To address the limitations of CI and CD approaches by integrating effective channel management.
• To improve both the accuracy and computational efficiency of multivariate time series forecasting.

Main Methods:

• Developed Adapformer, a Transformer-based framework with a dual-stage encoder-decoder architecture.
• Introduced the Adaptive Channel Enhancer (ACE) to enrich token representations by selectively incorporating dependencies.
• Implemented the Adaptive Channel Forecaster (ACF) to refine predictions by focusing on relevant covariates, reducing noise.

Main Results:

• Adapformer demonstrated superior performance compared to existing MTSF models across diverse datasets.
• The proposed model achieved enhanced predictive accuracy.
• Significant improvements in computational efficiency were observed with Adapformer.

Conclusions:

• Adapformer offers a state-of-the-art solution for MTSF by effectively managing channel dependencies.
• The framework successfully merges the benefits of CI and CD strategies.
• Adapformer represents a significant advancement in accurate and efficient multivariate time series forecasting.