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In order to make big data play a greater value in financial markets, exploring a new data processing method to improve the ability to detect structural mutations is necessary for the diagnosis and monitoring of financial data and investment direction. This article applies financial time series data, based on the moving sum (MOSUM) statistics, empirically analyzes changes of stock data in Kweichow Moutai in the past ten years. Detecting the feasibility of two multiscale MOSUM algorithms of bottom-up merging and local pruning and visualizing the results by numerical simulation, combined with the fact of the time point, we proved the consistent advantages of the two algorithms in detecting the accuracy and precision of variable points.

The empirical analysis of the two types of change points estimation algorithms based on MOSUM