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arXiv 2403.11044cs.LG
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推进多元时间序列相似性评估：一种综合计算方法

Advancing multivariate time series similarity assessment: an integrated computational approach

International Centre of Insect Physiology and Ecology (icipe)（国际昆虫生理与生态中心）；Faculty of Science, University of Dschang（大学丁加角学院）；University of KwaZulu-Natal（夸祖鲁-纳塔尔大学）；African Union Inter-African Phytosanitary Council (AU-IAPSC)（非洲联盟国际非洲植物检疫委员会）

Franck Tonle, Henri Tonnang, Milliam Ndadji, Maurice Tchendji, Armand Nzeukou, Kennedy Senagi, Saliou Niassy

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AI总结：

本文提出综合计算方法MTASA，通过混合优化时间序列对齐与多处理引擎，有效解决多元时间序列相似性评估中的大数据集与时间错位挑战，准确率提升约1.5倍且速度翻倍。

摘要：

数据挖掘，特别是多元时间序列数据分析，在从复杂系统中提取洞察力并支持跨多个领域的知情决策方面发挥着至关重要的作用。然而，评估多元时间序列数据的相似性面临若干挑战，包括处理大型数据集、解决时间错位问题，以及需要高效且全面的分析框架。为了解决所有这些挑战，我们提出了一种新颖的综合计算方法，称为 Multivariate Time series Alignment and Similarity Assessment (MTASA)。MTASA 建立在旨在优化时间序列对齐的混合方法论之上，并辅以一个增强计算资源利用率的多处理引擎。这种综合方法包含四个关键组件，每个组件都处理时间序列相似性评估的基本方面，从而提供了一个全面的分析框架。MTASA 被实现为一个具有用户友好界面的开源 Python 库，使其对研究人员和从业者易于使用。为了评估 MTASA 的有效性，我们使用真实世界环境数据开展了一项专注于评估农业生态系统相似性的实证研究。该研究的结果突显了 MTASA 的优越性，与现有的最先进多元时间序列相似性评估综合框架相比，其准确率提高了约 1.5 倍，速度提高了两倍。希望 MTASA 能显著提高多元时间序列分析的效率和可及性，使各个领域的研究人员和从业者受益。其在处理大型数据集、解决时间错位问题和提供准确结果方面的能力，使 MTASA 成为在复杂系统中提取洞察力和辅助决策过程的宝贵工具。

英文摘要：

Data mining, particularly the analysis of multivariate time series data, plays a crucial role in extracting insights from complex systems and supporting informed decision-making across diverse domains. However, assessing the similarity of multivariate time series data presents several challenges, including dealing with large datasets, addressing temporal misalignments, and the need for efficient and comprehensive analytical frameworks. To address all these challenges, we propose a novel integrated computational approach known as Multivariate Time series Alignment and Similarity Assessment (MTASA). MTASA is built upon a hybrid methodology designed to optimize time series alignment, complemented by a multiprocessing engine that enhances the utilization of computational resources. This integrated approach comprises four key components, each addressing essential aspects of time series similarity assessment, thereby offering a comprehensive framework for analysis. MTASA is implemented as an open-source Python library with a user-friendly interface, making it accessible to researchers and practitioners. To evaluate the effectiveness of MTASA, we conducted an empirical study focused on assessing agroecosystem similarity using real-world environmental data. The results from this study highlight MTASA's superiority, achieving approximately 1.5 times greater accuracy and twice the speed compared to existing state-of-the-art integrated frameworks for multivariate time series similarity assessment. It is hoped that MTASA will significantly enhance the efficiency and accessibility of multivariate time series analysis, benefitting researchers and practitioners across various domains. Its capabilities in handling large datasets, addressing temporal misalignments, and delivering accurate results make MTASA a valuable tool for deriving insights and aiding decision-making processes in complex systems.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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