A Versatile Approach to Classification of Multivariate Time Series Data

被引：12
|
作者：
Zagorecki, Adam [1 ]
机构：
[1] Cranfield Univ, Def Acad United Kingdom, Ctr Simulat & Analyt, Shrivenham SN6 8LA, Wilts, England
来源：
PROCEEDINGS OF THE 2015 FEDERATED CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SYSTEMS | 2015年 / 5卷
关键词：
D O I：
10.15439/2015F419
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
During the recent decade we have experienced a rise of popularity of sensors capable of collecting large amounts of data. One of most popular types of data collected by sensors is time series composed of sequences of measurements taken over time. With low cost of individual sensors, multivariate time series data sets are becoming common. Examples can include vehicle or machinery monitoring, sensors from smartphones or sensor suites installed on a human body. This paper describes a generic method that can be applied to arbitrary set of multivariate time series data in order to perform classification or regression tasks. This method was applied to the 2015 AAIA Data Mining Competition concerned with classifying firefighter activities and consecutively led to achieving the second-high score of nearly 80 participant teams.
引用
收藏
页码：407 / 410
页数：4
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