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Enhanced symbolic aggregate approximation method for financial time series data representation

机译：用于财务时间序列数据表示的增强符号聚合近似方法

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摘要

Data representation is one of the most important tasks in time series data pre-processing. Time series data representation is required to make the data more suitable for data mining specifically for prediction. Time series data is characterized by its numerical and continuous values. One of the data representation methods for time series is the Symbolic Aggregate Approximation (SAX) which uses mean values as the basis of representation of the data. However. representing the time series financial data with the mean value often causes the loss of patterns that can describes important pieces of information. The aim of this study is to propose an enhancement of SAX representation purposely for the financial time series data. The Enhanced SAX (EN-SAX) adds two new values to the original mean value for each segment in SAX. These values enable better representation for each segment in a lower dimension and keep some of the important patterns that are meaningful in financial time series data. The experimental results show that the EN-SAX representation manages to give lower error rates compared to SAX and improves the prediction accuracy.
机译：数据表示是时间序列数据预处理中最重要的任务之一。时间序列数据表示需要使数据更适合于专门用于预测的数据挖掘。时间序列数据的特征在于其数值和连续值。时间序列的数据表示方法之一是使用平均值作为数据的基础的符号聚合近似（SAX）。然而。代表具有平均值的时间序列金融数据通常会导致丢失可以描述重要信息的模式。本研究的目的是提出在财务时间序列数据的目的提高萨克斯表示。增强的SAX（EN-SAX）为SAX中的每个段的原始平均值增加了两个新值。这些值为较低维度的每个段提供更好的表示，并保留一些在财务时间序列数据中有意义的重要模式。实验结果表明，与SAX相比，ZAAX表示的设法使得较低的误差率并提高预测精度。

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