EEMD-neural network based real-time data abnormal value detection method

Abstract

An EEMD neural network based real-time data abnormal value detection method is provided, and the problem that the existing real-time data abnormal value detection method does not consider the historical data abnormal value is taken into account. The method comprises: obtaining historical time series data, and sorting the historical data according to a chronological order; using a median method to carry out preliminary detection on the historical data; finely detecting the historical data by using an EEMD method, and replacing the detected abnormal value with a zero value; then using the curve fitting method to fill the zero value, that is, correcting the abnormal value, and obtaining historical data closer to the objective reality after carrying out abnormal value detection and correction; and finally, by learning the historical data, using the neural network method so that the to-be-reported real-time data can be more accurately predicted, comparing the predicted value with the real-time reported monitored value so that whether there is abnormality can be determined, and correcting the abnormality. The method can be used to detect the real-time data abnormal value in one-dimensional time series, and is applicable to a wide range of fields such as water resources, traffic, weather, thermal power generation and other real-time monitoring data abnormal value detection.

Description

technical field [0001] The invention relates to a real-time data abnormal value detection method based on EEMD-neural network, which belongs to the field of data analysis. Background technique [0002] For the research on outlier detection methods of real-time data, predecessors have proposed methods such as neural network and support vector machine, but did not consider the outlier processing of historical data, because accurate prediction of real-time data needs to be based on reliable historical data . For historical data outlier detection, commonly used methods include outlier detection methods based on statistics, clustering, distance, density, etc., but these methods do not consider the time series change characteristics of time series data, but consider the complete set of data, hidden in the local outliers are difficult to detect. The present invention adopts the median-EEMD method for historical data to effectively detect local abnormal values ​​of time series, an...

AI Technical Summary

Problems solved by technology

[0008] The technical solution problem of the present invention is: overcome the deficiency of prior art, provide a kind of real-time data out

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