Characteristic Representation of Stock Time Series Data Based on Trend Extreme Points of K-line Combinations
Lei Han, Xuedong Gao, Haining Yang · Journal of Computing and Information Technology · 2025
The study of stock time series has been an important area of research in economics, finance, and management. Time series feature representation serves as the primary approach for studying time series. The K-line chart is a common representation of stock time series data. This study proposes a segmented representation method KCTEP based on the extreme points of stock K-line portfolio trend for K-line chart data, which is validated in sequence compression rate and distance metric. The experimental results show that the KCTEP method significantly improves the trend description by 8.63% and the compression rate by 2.95% when compared with the uniform extreme point representation method and the piecewise aggregation approximation method (PAA).The results lead to significant enhancement of the trend description effect and reduction of the distance metric error.