Abstract
Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation.
Highlights
Temporal data and temporal data mining which reflect the dynamic nature of data are one of the focuses of academic community in recent years
This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis
Time series similarity has been widely used in speech recognition
Summary
Temporal data and temporal data mining which reflect the dynamic nature of data are one of the focuses of academic community in recent years. Time series is an important temporal data. Time series similarity has been widely used in speech recognition. Euclidean distance and dynamic time warping are two classic methods. Euclidean distance is most frequently used in the time series, but Euclidean distance is a very brittle distance measure [1]. DTW is an algorithm for measuring the similarity between two sequences which may vary in time or speed. A wellknown application has been automatic speech recognition [2,3,4], to cope with different speaking speeds. Since Agrawal et al first proposed overall matching algorithm of time series similarity search in 1993 [5], more and more scholars began to focus on temporal data mining study
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