Sync Without Guesswork: Incomplete Time Series Alignment

2025-12-20

AI Summary

Incomplete multivariate time series alignment poses significant challenges due to missing values and asynchronous timestamps; conventional imputation-based preprocessing often introduces systematic bias. This paper formally defines the incomplete multivariate time series alignment problem and proposes a constraint-optimization-based end-to-end modeling framework that bypasses explicit imputation. To balance accuracy and efficiency, we design three approximation algorithms: (i) dynamic programming with bounded search space, (ii) greedy pruning with error guarantees, and (iii) temporal structural consistency constraints leveraging inter-series dependencies. Extensive experiments on diverse real-world multi-source datasets demonstrate strong robustness: our method achieves superior alignment quality over state-of-the-art approaches—increasing the number of correctly aligned tuples by up to 37% and improving average alignment accuracy by 22.6%.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Multivariate time series alignment is critical for ensuring coherent analysis across variables, but missing values and timestamp inconsistencies make this task highly challenging. Existing approaches often rely on prior imputation, which can introduce errors and lead to suboptimal alignments. To address these limitations, we propose a constraint-based alignment framework for incomplete multivariate time series that avoids imputation and ensures temporal and structural consistency. We further design efficient approximation algorithms to balance accuracy and scalability. Experiments on multiple real-world datasets demonstrate that our approach achieves superior alignment quality compared to existing methods under varying missing rates. Our contributions include: (1) formally defining incomplete multiple temporal data alignment problem; (2) proposing three approximation algorithms balancing accuracy and efficiency; and (3) validating our approach on diverse real-world datasets, where it consistently outperforms existing methods in alignment accuracy and the number of aligned tuples.

Problem

• Align incomplete multivariate time series without imputation
• Ensure temporal and structural consistency in alignment
• Balance accuracy and scalability with efficient algorithms

Innovation

• Constraint-based alignment framework avoiding imputation
• Efficient approximation algorithms balancing accuracy scalability
• Superior alignment quality under varying missing rates