Abstract
Abstract In order to improve the detection and filtering ability for financial data, a data-filtering method based on mathematical probability statistical model, a descriptive statistical analysis model of big data filtering, probability density characteristic statistical design data filtering analysis combined with fuzzy mathematical reasoning, regression analysis according to probability density of financial data distribution, and threshold test and threshold judgment are conducted to realize data filtering. The test results show that the big data filtering and the reliability and convergence of the mathematical model are optimal.
Highlights
As the size of the big data information is increasing, big data containing different types of information require effective filtration and separation of different big data, improvement in the storage and management capabilities of data, optimization of the storage space of data, and improvement in large data information identification
The filter model for data is based on mathematical statistical analysis and applies the statistical regression analysis model of data filtration
Based on the design of the probability mathematical model, combined with the pattern identification and the characteristic clustering method, data filtering is done; but the traditional method has a large interference with mode identification during data filtration
Summary
As the size of the big data information is increasing, big data containing different types of information require effective filtration and separation of different big data, improvement in the storage and management capabilities of data, optimization of the storage space of data, and improvement in large data information identification. The filter model for data is based on mathematical statistical analysis and applies the statistical regression analysis model of data filtration. Based on the design of the probability mathematical model, combined with the pattern identification and the characteristic clustering method, data filtering is done; but the traditional method has a large interference with mode identification during data filtration. In response to the above problem, this paper proposes a data filtration method based on the probability mathematical model, and the statistical characteristic analysis model for large data filtration is constructed, combined with mathematical modeling and statistical analysis methods, and the data filter probability mathematical model is optimized. There are many kinds of financial data, such as stock, futures, exchange rates, etc., characterized by nonlinear dynamic changes, caused because of the social development and economic policies of various countries. When they are financial, statistical or computer scholars hope to establish a relatively reliable model through a large amount of financial data to predict and analyze financial markets, thereby reducing investors’ mistakes in transaction decision-making, avoiding operational risks
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