Chaotic time sequence prediction method based on attention mechanism deep learning

A chaotic time series and deep learning technology, applied in neural learning methods, forecasting, biological neural network models, etc., can solve problems such as affecting the prediction effect of chaotic time series, distracting attention, affecting prediction accuracy and wide application.

Pending Publication Date: 2020-06-09
DALIAN UNIV OF TECH
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Problems solved by technology

Nevertheless, the traditional neural network learning algorithm based on gradient descent still has some obvious drawbacks and limitations in the application of chaotic time series forecasting, including: (1) These models converge slowly and tend to fall into local optimum, which affects the Predictive Accuracy and Wide Application
(2) Due to the static network, these models have limitations in identifying chaotic dynamical systems
(3) For these networks, determining an appropriate number of hidden nodes is also a c

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  • Chaotic time sequence prediction method based on attention mechanism deep learning
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  • Chaotic time sequence prediction method based on attention mechanism deep learning

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Embodiment Construction

[0042] The present invention will be further described below in conjunction with accompanying drawing.

[0043] like figure 1 As shown, a chaotic time series prediction method based on deep learning of attention mechanism includes the following steps:

[0044] Step 1. Construct a chaotic time series data set, and use the Lorenz system and Rossler system to generate chaotic time series data, which specifically includes the following sub-steps:

[0045] (a) The Lorenz system equation is described by formula (1),

[0046]

[0047] In the formula, and Indicates the derivative of the independent variable time t, a, b, c are Lorenz system parameter constants, set the initial value as a=16, b=4, c=45.92, x, y, z represent the state of the Lorenz system, set the initial value For x(0)=y(0)=z(0)=1.0, use the fourth-order Runge-Kutta method to generate a chaotic time series with Δt as the time interval;

[0048] (b) In order to make the Lorenz system completely enter the chaot...

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Abstract

The invention belongs to the technical field of chaotic systems. The invention discloses a chaotic time sequence prediction method based on attention mechanism deep learning. The method comprises thefollowing steps: (1) constructing a chaotic time sequence data set; (2) carrying out phase space reconstruction on a chaotic time sequence; (3) training chaotic time sequence data by using an LSTM neural network model; (4) constructing a prediction-based attention mechanism model, (5) constructing an offline training model, and (6) carrying out online prediction. The chaotic time sequence prediction method based on attention mechanism deep learning is clear in model structure, has a reference value, and can be applied to the aspects of financial market prediction or energy prediction and the like of a chaotic system.

Description

technical field [0001] The invention relates to a chaotic time series prediction method based on attention mechanism deep learning, belonging to the technical field of chaotic systems. Background technique [0002] Chaotic system is a kind of orderly high-dimensional complex nonlinear dynamical system generated from disordered motion. The discrete situation of chaos is often manifested as chaotic time series. Chaotic time series is a time series with chaotic characteristics generated by chaotic systems. Chaotic time series contains very rich dynamic information of the system. Chaotic time series is the path from chaos theory to reality. A bridge to the world is an important research field of chaotic systems. According to Takens phase space delay reconstruction theorem, the internal law of chaotic system can be reconstructed and predicted by chaotic time series. How to choose or construct a forecasting model is one of the key issues in chaotic time series forecasting. [0...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06N3/08
CPCG06Q10/04G06N3/08G06N3/044G06N3/045
Inventor 孙媛媛王博林张书晨陈彦光
Owner DALIAN UNIV OF TECH
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