应用自然启发的优化算法选择重要的时间戳以减少时间序列的重复性
Applying Nature-Inspired Optimization Algorithms for Selecting Important Timestamps to Reduce Time Series Dimensionality
摘要：
时间序列数据占当今数据供应的主要部分。时间序列挖掘处理几个任务，如分类，聚类，按内容查询，预测等。对原始时间序列执行数据采掘任务是低效的，因为这些数据本质上是高维的。相反，在对时间序列执行不同的数据采掘任务之前，首先使用几种技术对时间序列进行预处理。一般来说，有两种主要的方法来降低时间序列的维数，第一种是我们所说的地标方法。这些方法是基于在目标时间序列中找到特征。第二是基于数据转换。这些方法将时间序列从原始空间转换到一个简化的空间，在那里可以更有效地管理它们。我们在本文中提出的方法应用了第三种方法，因为它通过选择时间序列中的重要点将时间序列投影到低维空间。我们的方法的新颖之处在于，这些点不是根据几何标准选择的，这在大多数情况下是主观的，而是通过优化过程。我们的方法的另一个重要特征是，这些重要的点是在一个队列级别上选择的，而不是在一个单一的时间序列级别。这种策略的直接优点是，在低维空间上定义的距离是应用于原始数据的原始距离的下限。这使我们能够应用流行的GEMINI算法。我们的实验结果的各种各样的时间序列数据集，使用不同的优化，并应用于两个主要的数据采掘任务，验证了我们的新方法。
英文摘要：
Time series data account for a major part of data supply available today. Time series mining handles several tasks such as classification, clustering, query-by-content, prediction, and others. Performing data mining tasks on raw time series is inefficient as these data are high-dimensional by nature. Instead, time series are first pre-processed using several techniques before different data mining tasks can be performed on them. In general, there are two main approaches to reduce time series dimensionality, the first is what we call landmark methods. These methods are based on finding characteristic features in the target time series. The second is based on data transformations. These methods transform the time series from the original space into a reduced space, where they can be managed more efficiently. The method we present in this paper applies a third approach, as it projects a time series onto a lower-dimensional space by selecting important points in the time series. The novelty of our method is that these points are not chosen according to a geometric criterion, which is subjective in most cases, but through an optimization process. The other important characteristic of our method is that these important points are selected on a dataset-level and not on a single time series-level. The direct advantage of this strategy is that the distance defined on the low-dimensional space lower bounds the original distance applied to raw data. This enables us to apply the popular GEMINI algorithm. The promising results of our experiments on a wide variety of time series datasets, using different optimizers, and applied to the two major data mining tasks, validate our new method.
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。
