Abstract
Focusing on problems existing in track irregularity time series data quality, this paper first presents abnormal data identification, data offset correction algorithm, local outlier data identification, and noise cancellation algorithms. And then proposes track irregularity time series decomposition and reconstruction through the wavelet decomposition and reconstruction approach. Finally, the patterns and features of track irregularity standard deviation data sequence in unit sections are studied, and the changing trend of track irregularity time series is discovered and described.
Highlights
Time series [1,2,3] is a statistical method of econometrics
Time series does not study the interdependence causality between things, and the study is based on the assumption that some of the information which comes from the historical data can be used to explain the current situation and to predict the future of time series
Time series forecasting is an active study area, and there are a lot of literature on it
Summary
Time series [1,2,3] is a statistical method of econometrics. Time series studies the changes showed by observation values of a certain variable in the system in chronological order during a given period and tries to find out the characteristics, future trends and laws over time and the laws are often the consolidated results of impacts by a variety of other factors. In the researching methods of time series forecasting, collection and analysis of historical observations are used to determine the model and to capture the generating process of underlying data, and the model is used to make prediction. Based on the importance of data, this paper identifies abnormal data and calibrates offset data and segment data in order to study track irregularity change trends In this context, this paper analyses track irregularity data, explores the underlined rules of track irregularity change, predicts future trends, and, provides data and models support of track state changes to relevant railway departments, to ensure railway transportation safety. Track irregularity data is provided by State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University
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