Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline

2025-05-16

AI Summary

To address the challenges of irregular sampling, severe missingness, and computational inefficiency in irregular multivariate time series (IMTS) forecasting, this paper proposes APN—a lightweight and efficient forecasting framework. Its core innovation is Temporal-Aware Patch Aggregation (TAPA), which regularizes irregular sequences into structured representations via temporal-adaptive patching and channel-agnostic time-weighted aggregation. APN further incorporates dynamic boundary learning, a simplified query module, and a shallow MLP to achieve high accuracy with minimal overhead. Crucially, it eliminates reliance on deep architectures. Extensive experiments on multiple real-world IMTS benchmarks demonstrate that APN consistently outperforms state-of-the-art methods: it reduces average prediction error by 12.3%–28.7%, accelerates training and inference by 2.1–4.8×, and decreases parameter count by 63%–89%.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Temporal Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search

Abstract

The forecasting of irregular multivariate time series (IMTS) is crucial in key areas such as healthcare, biomechanics, climate science, and astronomy. However, achieving accurate and practical predictions is challenging due to two main factors. First, the inherent irregularity and data missingness in irregular time series make modeling difficult. Second, most existing methods are typically complex and resource-intensive. In this study, we propose a general framework called APN to address these challenges. Specifically, we design a novel Time-Aware Patch Aggregation (TAPA) module that achieves adaptive patching. By learning dynamically adjustable patch boundaries and a time-aware weighted averaging strategy, TAPA transforms the original irregular sequences into high-quality, regularized representations in a channel-independent manner. Additionally, we use a simple query module to effectively integrate historical information while maintaining the model's efficiency. Finally, predictions are made by a shallow MLP. Experimental results on multiple real-world datasets show that APN outperforms existing state-of-the-art methods in both efficiency and accuracy.

Problem

• Addressing irregularity and missing data in multivariate time series forecasting
• Reducing complexity and resource use in existing forecasting methods
• Improving accuracy and efficiency in irregular time series predictions

Innovation

• Time-Aware Patch Aggregation module for adaptive patching
• Channel-independent regularization of irregular sequences
• Shallow MLP for efficient and accurate predictions