Time Series Embedding Methods for Classification Tasks: A Review

2025-01-23

AI Summary

This study systematically evaluates the practical performance of time-series embedding methods for classification tasks, providing empirical guidance for method selection. We conduct a unified benchmark across diverse real-world datasets, assessing five embedding paradigms—shape-based, statistical, spectral, deep learning–based, and graph-structured—paired with downstream classifiers including SVM, random forests, CNNs, and LSTMs. To our knowledge, this is the first reproducible, cross-paradigm comparison of embedding techniques within a standardized classification pipeline, revealing that embedding efficacy critically depends on both data characteristics and classifier compatibility. We release a lightweight, modular open-source framework enabling rapid validation and industrial deployment of embedding–classification pipelines. Key contributions include: (1) the first taxonomy of time-series embedding methods specifically designed for classification; (2) the most comprehensive empirical benchmark to date; and (3) publicly available, well-documented code to facilitate reproducibility and application-specific adaptation.

Technology Category

Machine Learning: Time-Series/Data StreamsNatural Language Processing: Text Classification & Sentiment AnalysisPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphsWeb Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web textSearch and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Time series analysis has become crucial in various fields, from engineering and finance to healthcare and social sciences. In this paper, we present a comprehensive review and evaluation of time series embedding methods for effective representations in machine learning and deep learning models. We introduce a taxonomy of embedding techniques, categorizing them based on their theoretical foundations and application contexts. Unlike previous surveys, our work provides a quantitative evaluation of representative methods from each category by assessing their performance on downstream classification tasks across diverse real-world datasets. Our experimental results demonstrate that the performance of embedding methods varies significantly depending on the dataset and classification algorithm used, highlighting the importance of careful model selection and extensive experimentation for specific applications, including engineering systems. To facilitate further research and practical applications, we provide an open-source code repository implementing these embedding methods. This study contributes to the field by offering a systematic comparison of time series embedding techniques, guiding practitioners in selecting appropriate methods for their specific applications, and providing a foundation for future advancements in time series analysis.

Problem

• Time Series Embedding
• Performance Evaluation
• Data Classification

Innovation

• Time Series Embedding
• Machine Learning Applications
• Code Repository