MODWT-EMD-based time sequence hybrid prediction method

Abstract

The invention provides a MODWT-EMD-based time sequence hybrid prediction method to solve the problem for predication of a time sequence. The method comprises the following steps: firstly, performing maximum overlapping discrete wavelet decomposition on an original time sequence to obtain N frequency components; secondly, performing empirical mode decomposition on the obtained N frequency components to obtain a plurality of IMF components and residual errors; inputting a plurality of IMF components and residual errors obtained by decomposition into a random forest classifier, scoring and sorting the importance of each IMF component and residual error, and selecting feature information with relatively large influence; and finally, inputting the selected feature information into Bi-GRU for training, predicting an original time sequence by using a trained model, and evaluating the prediction capability of the method through a mean absolute error, a root-mean-square error, a mean absolute percentage error and goodness of fit. The prediction result can be obtained with less training time on the premise of ensuring the prediction accuracy.

Description

technical field [0001] The invention relates to the field of wireless sensor networks, and specifically relates to a time series mixed prediction method based on MODWT-EMD. Background technique [0002] In recent years, with the continuous development of the industrial Internet and the increasing demand for data analysis in industrial applications, the number of sensor nodes in wireless sensor networks has gradually increased. At the same time, the data collected by sensors is growing explosively, and massive data needs to be collected, transmitted, stored and analyzed through sensor nodes. In the process of collecting data by sensor nodes, it is inevitable to face problems such as data transmission quality, sensor energy consumption and network congestion. The dense deployment of nodes in the sensor network and the amount of data communicated between sensor nodes will explode with the continuous expansion of the network scale. With the continuous expansion of the network ...

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Problems solved by technology

[0007] The main problem to be solved by the present invention is: Aiming at the problems of long training time and complex parameter adjustment of the existing time series forecasting method based on machine learning, a mixed forecasting method of time series based on MODWT-EMD is proposed

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