A novel approach for nonstationary time series analysis with time-invariant correlation coefficient
type: Item
instance of: scholarly article
title: A novel approach for nonstationary time series analysis with time-invariant correlation coefficient
zbMATH Open document ID: 1407.62321
DOI: 10.1155/2014/148432
author: Chengrui Liu
author: Huimin Fu
author: Yongbo Zhang
author: Zhihua Wang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: We will concentrate on the modeling and analysis of a class of nonstationary time series, called correlation coefficient stationary series, which commonly exists in practical engineering. First, the concept and scope of correlation coefficient stationary series are discussed to get a better understanding. Second, a theorem is proposed to determine standard deviation function for correlation coefficient stationary series. Third, we propose a moving multiple-point average method to determine the function forms for mean and standard deviation, which can help to improve the analysis precision, especially in the context of limited sample size. Fourth, the conditional likelihood approach is utilized to estimate the model parameters. In addition, we discuss the correlation coefficient stationarity test method, which can contribute to the verification of modeling validity. Monte Carlo simulation study illustrates the authentication of the theorem and the validity of the established method. Empirical study shows that the approach can satisfactorily explain the nonstationary behavior of many practical data sets, including stock returns, maximum power load, China money supply, and foreign currency exchange rate. The effectiveness of these processes is addressed by forecasting performance.
Mathematics Subject Classification ID: 62M10
zbMATH DE Number: 7015789
Wikidata QID: Q59063223
describes a project that uses: FinTS
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/148432
OpenAlex ID: W2013168918
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description: scientific article; zbMATH DE number 7015789