MODWT-EMD-based time sequence hybrid prediction method

A time series and hybrid prediction technology, applied in neural learning methods, biological neural network models, etc., can solve the problems of long training time and complex parameter adjustment, so as to improve the extraction ability, reduce the difficulty of adjusting parameters, and reduce the training time. Effect

A time series and hybrid prediction technology, applied in neural learning methods, biological neural network models, etc., can solve the problems of long training time and complex parameter adjustment, so as to improve the extraction ability, reduce the difficulty of adjusting parameters, and reduce the training time. Effect

CN113222145APending Publication Date: 2021-08-06XIAN UNIV OF POSTS & TELECOMM
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  • MODWT-EMD-based time sequence hybrid prediction method
  • MODWT-EMD-based time sequence hybrid prediction method
  • MODWT-EMD-based time sequence hybrid prediction method

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Embodiment Construction

[0032] The embodiments of the present invention will be further described below in conjunction with the examples, but the implementation of the present invention is not limited thereto. In the following description, the parts that are not specifically described in detail are the parts that can be understood and implemented by those of ordinary skill with reference to the prior art, such as the general execution steps of the random forest.

[0033] Such as figure 1 , a mixed forecasting method for time series based on MODWT-EMD includes the following steps:

[0034] (1) Obtain multiple frequency components of the time series.

[0035] Decompose the time series by MODWT to get figure 2 Multiple frequency components are shown, and each frequency component contains characteristic information of the time series on different time scales.

[0036] (2) Decompose multiple frequency components to obtain time series features.

[0037] The multiple frequency components obtained in (1...

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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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Application Information

Patent Timeline
06 Aug 2021
Publication
CN113222145A
IPC
G06N3/08
CPC
G06N3/08
Inventors
高聪; 贾靖文