Ship flow prediction method based on improved EEMD-IndRNN

Abstract

The invention discloses a ship flow prediction method based on improved EEMD-IndRNN. The method comprises: using a set empirical mode decomposition algorithm to decompose non-linear and non-stationaryship flow data into a series of high and low frequency intrinsic mode function sequences with stationarity and a monotonous remainder sequence, so that information of an original sequence is reservedto the maximum extent, the internal rule of the sequence is fully utilized, and the prediction precision is improved; then calculating the correlation between each component and the original ship flow data by using a Pearson correlation coefficient, and recombining the components into three new components of high, middle and low according to the correlation; and finally, separately processing thecomponents by using an independent recurrent neural network, constructing a deep learning neural network by superposing a plurality of hidden layers, and fully extracting time hidden feature information of the ship flow in data training in combination with a large amount of ship flow data to complete prediction. According to the method, data component processing is refined, prediction precision is improved, and better adaptability is achieved.

Description

technical field [0001] The invention relates to the technical field of time series prediction, in particular to a method for predicting ship flow based on an improved EEMD-IndRNN (ensemble empirical mode division-independent recurrent neural network). Background technique [0002] With the development of my country's maritime economy and trade, the number of ships is gradually increasing. In order to solve the problems of ship traffic accidents caused by the increase in the density of water routes, channel planning and improve the efficiency of ship traffic, scientific and accurate prediction of ship flow is required. Nowadays, due to the rapid development of science and technology and higher forecast accuracy requirements, a single forecast method for ship flow can no longer meet people's needs, usually an effective combination of multiple forecast algorithms. [0003] The research on ship traffic flow forecasting is mainly divided into: one is to seek a unique internal rel...

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Problems solved by technology

[0005] The object of the present invention is to provide a kind of ship flow forecasting method based on improved EEMD-IndRNN (ensemble empirical mode and independent cyclic neural network), which belongs to the framework of deep neural network method, and can solve the problem of time series prediction for different time scales Adaptability issues while improving prediction accuracy

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