论文标题
基于模式的时间序列分析：估计
Pattern-Based Analysis of Time Series: Estimation
论文作者
论文摘要
尽管物联网（IoT）设备和传感器创建了连续的信息流，但大数据基础架构被视为实时处理数据的涌入。这种连续信息流的一种类型是时间序列数据。由于时间序列中的信息丰富以及汇总统计数据不足以将结构和模式封装在此类数据中，因此开发了学习时间序列的新方法。在本文中，我们提出了一种称为“模式树”的新方法，以使用二进制结构树在时代系列中学习模式。尽管图案树可以用于许多目的，例如无损压缩，预测和异常检测，但在本文中，我们将其专注于时间序列估计和预测。与其他方法相比，我们提出的图案树方法改善了估计的平均平方误差。
While Internet of Things (IoT) devices and sensors create continuous streams of information, Big Data infrastructures are deemed to handle the influx of data in real-time. One type of such a continuous stream of information is time series data. Due to the richness of information in time series and inadequacy of summary statistics to encapsulate structures and patterns in such data, development of new approaches to learn time series is of interest. In this paper, we propose a novel method, called pattern tree, to learn patterns in the times-series using a binary-structured tree. While a pattern tree can be used for many purposes such as lossless compression, prediction and anomaly detection, in this paper we focus on its application in time series estimation and forecasting. In comparison to other methods, our proposed pattern tree method improves the mean squared error of estimation.
