时间序列数据的分割与聚类

S. Tiwari
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引用次数: 0

摘要

分析时间序列数据引起了极大的兴趣。为了研究基本机制,识别和测量模式,决定事情,预测即将发生的事情,还创建了几种方法来从数据中理解见解并获得重要的统计数据。机器学习和表示的目标都是从数据中提取信息。为了最大限度地发挥人和计算机的同等作用,如果能将有效的机器学习算法(分组和分割)集成到通信平台中,那将是最理想的。这种分析也有助于电力负荷和风能的预测。本研究旨在定义时间序列数据聚类或分类方法背后的关键思想。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Segmentation and Clustering of Time Series Data
Analyzing time series data had attracted a great deal of interest. To study the basic mechanisms, recognize and measure patterns, decide things, and forecast the upcoming, several ways have also been created to comprehend the insights from the data and get important statistics. The objective of both machine learning as well as representation is information extraction from data. To get the greatest out of equal people and computers, it would be ideal if effective machine learning algorithms (grouping and segmentation) could be integrated into communication platforms. This analysis is also helpful for forecasting electricity load and wind energy. This study aims to define the key ideas behind time series data clusters or classification methods.
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