Graphical Analysis Method of Time Series Data Based on Automatic Encoding Technology with Packet Loss

Abstract

The invention discloses a time series data graphics analysis method based on automatic coding technology with packet loss. The time series data graphics analysis method comprises the following steps: 1) data preprocessing: converting time series data into a specific image format; 2) pre-training: extracting the graphic features of a time series through the automatic coding technology with the packet loss; 3) classifier training: carrying out classifier training to coding machine weight and a training sample class identifier in a pre-training process; and 4) application: realizing the functions of similarity matching and classification of the time series by utilizing the trained classifier. A defect that a traditional time series analysis method is very sensitive to data change since the traditional time series analysis method pays attention to the data feature of the time series is overcome, and a visual processing method of the time series data by people is simulated. On an aspect of the similarity matching, the invention exhibits high accuracy and low time complexity. In classification, high classification precision is guaranteed, and the invention also exhibits good universality and robustness to different types of time series data.

Description

technical field [0001] The invention relates to a graphical analysis method of time series data based on automatic coding technology with packet loss. The method is inspired by the data processing method of human vision, and the traditional time series analysis method pays attention to the data characteristics of the time series and changes the data. Very sensitive shortcomings, using the stacked auto-encoding technology with packet loss to automatically learn the graphical features of time series data, and re-abstract the time series data, and then use the learned features for the error backpropagation neural network classifier Training, and then realize the similarity matching and classification function of time series data, which belongs to the field of data mining and machine learning. Background technique [0002] In the past two decades, different time series analysis and mining techniques have been continuously produced. These techniques mainly focus on similarity ma...

AI Technical Summary

Problems solved by technology

The biggest difference between the present invention and the existing method is that the present invention performs time series analysis based on the image features of the time series data, which overcomes the disadvantage that the existing time series data analysis method only has good performance for specific data forms and has no universality. In similarity matching, the present invention has good accuracy and extremely low time complexity compared with existing methods, and in classification, it is more robust than existing methods, and its classification accuracy is higher than that of different data sets. The overall performance is also very good

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