A Combination of Fuzzy Techniques and Chow Test to Detect Structural Breaks in Time Series

In a series of papers, we suggested a non-statistical method for the detection of structural breaks in a time series. It is based on the applications of special fuzzy modeling methods, namely Fuzzy transform (F-transform) and selected methods of Fuzzy Natural Logic (FNL). In this paper, we combine o...

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Bibliographic Details
Published in:Axioms 2023-01, Vol.12 (2), p.103
Main Authors: Novák, Vilém, Truong, Thi Thanh Phuong
Format: Article
Language:English
Subjects:
Chow test
evaluative linguistic expressions
F-transform
Fuzzy algorithms
Fuzzy logic
Fuzzy sets
Fuzzy systems
fuzzy transform
Hypotheses
Linguistics
Mathematical research
Natural language
Semantics
Statistical methods
Statistical tests
structural breaks
Tangents
Time series
Time-series analysis
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Summary:In a series of papers, we suggested a non-statistical method for the detection of structural breaks in a time series. It is based on the applications of special fuzzy modeling methods, namely Fuzzy transform (F-transform) and selected methods of Fuzzy Natural Logic (FNL). In this paper, we combine our method with the principles of the classical Chow test, which is a well-known statistical method for testing the presence of a structural break. The idea is to construct testing statistics similar to that of the Chow test which is formed from components of the first-degree F-transform. These components contain an estimation of the average values of the tangents (slopes) of the time series over an imprecisely specified time interval. In this paper, we illustrate our method and its statistical test on a real-time series and compare it with three classical statistical methods.
ISSN:2075-1680
2075-1680
DOI:10.3390/axioms12020103