A Pattern Discovery Approach to Multivariate Time Series Forecasting

2022-12-20

arXiv.org

AI Summary

To address the challenges of global temporal modeling and cross-variable dependency capture in multivariate long-horizon time series forecasting under few-shot settings, this paper proposes a novel deep learning framework. Methodologically, it abandons conventional fixed-pattern assumptions and instead designs learnable, diverse pattern functions to adaptively discover subsequence-level temporal patterns; it further introduces a dynamic multivariate correlation matrix to model time-varying inter-variable dependencies. The core contribution lies in the first unified formulation of pattern discovery and dependency modeling as a learnable, diverse, and dynamically coupled mechanism—enabling subsequence-level global relational modeling. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method significantly improves prediction accuracy for long horizons (e.g., 96–192 steps) and high-dimensional multivariate series, achieving state-of-the-art performance.

Abstract

Multivariate time series forecasting constitutes important functionality in cyber-physical systems, whose prediction accuracy can be improved significantly by capturing temporal and multivariate correlations among multiple time series. State-of-the-art deep learning methods fail to construct models for full time series because model complexity grows exponentially with time series length. Rather, these methods construct local temporal and multivariate correlations within subsequences, but fail to capture correlations among subsequences, which significantly affect their forecasting accuracy. To capture the temporal and multivariate correlations among subsequences, we design a pattern discovery model, that constructs correlations via diverse pattern functions. While the traditional pattern discovery method uses shared and fixed pattern functions that ignore the diversity across time series. We propose a novel pattern discovery method that can automatically capture diverse and complex time series patterns. We also propose a learnable correlation matrix, that enables the model to capture distinct correlations among multiple time series. Extensive experiments show that our model achieves state-of-the-art prediction accuracy.

Problem

• Addressing few-shot time series forecasting challenges
• Capturing long-term dependencies in limited data
• Modeling diverse meta-knowledge for better accuracy

Innovation

• Meta-learning-based Gaussian process latent variable model
• Gaussian process kernel captures long-term dependencies
• Kernel Association Search enhances interpretability and accuracy