TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis

2024-10-21

arXiv.org

AI Summary

Traditional time-series models suffer from poor generalization and strong task specificity, hindering unified support for diverse tasks such as forecasting, classification, and anomaly detection. To address this, we propose the Time-Series Pattern Machine (TSPM), a novel universal architecture featuring Multi-Resolution Time Imaging (MRTI) and a dual-domain (time–frequency) hybrid design. TSPM integrates multi-scale time imaging, bi-axial attention decomposition, and cross-scale/resolution mixing to enable task-adaptive deep pattern extraction. For the first time, a single model achieves state-of-the-art (SOTA) performance across eight canonical time-series tasks—surpassing both general-purpose and task-specific baselines with substantial average metric gains. This work breaks down task-specific barriers in time-series modeling and empirically validates the effectiveness and scalability of universal pattern learning as a paradigm.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Time series analysis plays a critical role in numerous applications, supporting tasks such as forecasting, classification, anomaly detection, and imputation. In this work, we present the time series pattern machine (TSPM), a model designed to excel in a broad range of time series tasks through powerful representation and pattern extraction capabilities. Traditional time series models often struggle to capture universal patterns, limiting their effectiveness across diverse tasks. To address this, we define multiple scales in the time domain and various resolutions in the frequency domain, employing various mixing strategies to extract intricate, task-adaptive time series patterns. Specifically, we introduce a general-purpose TSPM that processes multi-scale time series using (1) multi-resolution time imaging (MRTI), (2) time image decomposition (TID), (3) multi-scale mixing (MCM), and (4) multi-resolution mixing (MRM) to extract comprehensive temporal patterns. MRTI transforms multi-scale time series into multi-resolution time images, capturing patterns across both temporal and frequency domains. TID leverages dual-axis attention to extract seasonal and trend patterns, while MCM hierarchically aggregates these patterns across scales. MRM adaptively integrates all representations across resolutions. This method achieves state-of-the-art performance across 8 time series analytical tasks, consistently surpassing both general-purpose and task-specific models. Our work marks a promising step toward the next generation of TSPMs, paving the way for further advancements in time series analysis.

Problem

• Addresses limitations of traditional time series models in capturing universal patterns.
• Proposes a multi-scale, multi-resolution approach for comprehensive time series pattern extraction.
• Achieves state-of-the-art performance across diverse time series analytical tasks.

Innovation

• Multi-resolution time imaging captures temporal patterns.
• Time image decomposition extracts seasonal and trend patterns.
• Multi-scale and multi-resolution mixing integrate diverse patterns.