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Time-Series Data Mining

机译：时间序列数据挖掘

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摘要

In almost every scientific field, measurements are performed over time. These observations lead to a collection of organized data called time series. The purpose of time-series data mining is to try to extract all meaningful knowledge from the shape of data. Even if humans have a natural capacity to perform these tasks, it remains a complex problem for computers. In this article we intend to provide a survey of the techniques applied for time-series data mining. The first part is devoted to an overview of the tasks that have captured most of the interest of researchers. Considering that in most cases, time-series task relies on the same components for implementation, we divide the literature depending on these common aspects, namely representation techniques, distance measures, and indexing methods. The study of the relevant literature has been categorized for each individual aspects. Four types of robustness could then be formalized and any kind of distance could then be classified. Finally, the study submits various research trends and avenues that can be explored in the near future. We hope that this article can provide a broad and deep understanding of the time-series data mining research field.
机译：在几乎每个科学领域，测量都是随着时间而进行的。这些观察结果导致收集了称为时间序列的有组织数据。时序数据挖掘的目的是尝试从数据的形状中提取所有有意义的知识。即使人类具有执行这些任务的天生能力，对于计算机来说仍然是一个复杂的问题。在本文中，我们打算对应用于时序数据挖掘的技术进行概述。第一部分专门介绍已引起研究人员大部分兴趣的任务。考虑到在大多数情况下，时间序列任务的实现依赖于相同的组件，因此我们根据这些常见方面（即表示技术，距离度量和索引方法）对文献进行划分。相关文献的研究已针对各个方面进行了分类。然后可以确定四种类型的鲁棒性，然后可以对任何距离进行分类。最后，该研究提出了可在不久的将来探索的各种研究趋势和途径。我们希望本文能够对时序数据挖掘研究领域提供广泛而深刻的理解。

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