Evaluation of the Fuzzy Correlation Coefficient for Interval Time Series Data
School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523109, China
| Abstract: | The development of the Internet has advanced data science while increasing the diversity and complexity of data types and integration processes. In this context, dependent fuzzy interval data has emerged as an important research topic. With the advent of big data, data generation has shifted from univariate observations to multi-valued representations. Accordingly, fuzzy data analysis methods based on multi-valued logic have been increasingly investigated for practical statistical applications. Since the advent of the big data era, the nature of data generation has gradually evolved from single-valued data to multi-valued data. To address the analytical needs of practical statistical applications, fuzzy data analysis methods based on multi-valued logic have been increasingly explored and developed. In this study, two algorithms are proposed for estimating correlation coefficients in interval time series data: the fuzzy interval autocorrelation coefficient and the fuzzy interval cross-correlation coefficient. These methods are developed to evaluate fuzzy correlations between random variables represented by count intervals, where count values are expressed as fuzzy interval data. Simulation experiments and numerical case analyses are conducted to assess the performance and accuracy of the proposed methods. The results indicate that the correlation coefficients obtained from fuzzy interval time series data are consistent with those derived from traditional univariate correlation methods. The proposed algorithms are also shown to effectively verify variations between two variables in fuzzy interval time series data. |
| Keywords: | Dependent Data; Fuzzy Interval Autocorrelation Coefficient; Fuzzy Interval Cross-Correlation Coefficient; Time Series |
| DOI: | 10.57237/j.wjms.2026.02.001 |
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Received: 3 June 2026
Accepted: 1 July 2026
Published Online: 28 July 2026
References
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