Communication, Computer and Automation Engineering
Optimal segmentation algorithm and empirical research for ordered samples in economic cycles
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Abstract:
Clustering often plays a foundational role in solving practical problems. The partitioning of economic cycles into stages represents a specialized clustering challenge, requiring the segmentation of sequential time series samples. The orderly division of economic cycle stages is fundamental to studying problems related to economic fluctuations. This paper presents an economic development indicator vector based on data from the gross domestic product(GDP)and consumer price index of residents(CPI). It introduces an optimal segmentation algorithm for ordered samples to partition economic cycle stages and analyzes the algorithm’s accuracy trend and optimal segmentation effect using sample data from the 3rd quarter of 1948 to the 2nd quarter of 2008 of the United States, and from the 3rd quarter of 1971 to the 2nd quarter of 2008 of Japan. The study offers an efficient and concise algorithm for delineating economic cycle stages.
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Supported by National Natural Science Foundation of China (71963037)， and Yunnan Provincial Philosophy and Social Science Innovation Team (2023CX05).
