Time series data prediction method based on time series decomposition and LSTM

A technology for time series and data prediction, applied in forecasting, data processing applications, neural learning methods, etc., can solve the problem of inability to predict nonlinear time series well, inaccurate prediction of non-stationary burst data, and inability to adapt to time series data Change characteristics and other issues to achieve the effect of solving traffic congestion, good prediction effect, and saving system energy consumption

Pending Publication Date: 2022-03-25
CHANGCHUN UNIV
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AI Technical Summary

Problems solved by technology

However, commonly used methods cannot predict complex nonlinear time series well, and the performance of deep learning methods has improved, but the training time is long and there is a phenomenon of overfitting
In addition, the problem of prediction lag still exists, and it cannot adapt to the changing characteristics of time series data, especially for non-stationary burst data.

Method used

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  • Time series data prediction method based on time series decomposition and LSTM
  • Time series data prediction method based on time series decomposition and LSTM
  • Time series data prediction method based on time series decomposition and LSTM

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Embodiment Construction

[0040] The present invention will be further described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can implement it with reference to the description.

[0041] Such as Figure 1-5 As shown, the present invention provides a time series data prediction method based on time series decomposition and LSTM, which uses time series decomposition to separate the trend and cycle of time data, and combines it with LSTM, thereby effectively improving the accuracy of prediction.

[0042] Use python software, version number: python3.7, tensorflow1.14, operating software environment: windows10, hardware configuration: processor AMD Ryzen 5 4600H with Radeon Graphics (12CPUs), 3.0GHz, memory 16G RAM, graphics card NVIDIA GeForce GTX 1650 ;

[0043] The first neural network model: such as figure 1 The LSTM network in the middle is shown: the first layer is the LSTM layer with 100 neurons. The second layer is a Dense layer with 50 neurons. ...

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Abstract

The invention discloses a time series data prediction method based on time series decomposition and LSTM, and the method comprises the steps: 1, collecting time series data, carrying out the preprocessing of the time series data, obtaining a time series sample set which meets the data demands of a prediction model, carrying out the division of a training set and a test set, and obtaining a first training set and a first test set; step 2, establishing a first neural network for trend component and remainder prediction based on LSTM, performing training and parameter adjustment through the first training set, predicting the first training set by using a trained first neural network model to obtain a trend component and remainder prediction result of the first training set, and further processing the prediction result into a second training set; step 3, establishing a second neural network based on ANN, and performing training and parameter adjustment through a second training set; and 4, performing joint prediction on the test set by using the trained first neural network model and the trained second neural network model to obtain a fitted time sequence data prediction result.

Description

technical field [0001] The invention relates to a time series data prediction method based on time series decomposition and LSTM, and belongs to the technical field of time series data prediction. Background technique [0002] Time series data forecasting is to simulate the change law of the data through the analysis of historical data, and then predict the data at the future time point. Time series forecasting is of great significance in many applications such as stock analysis, air pollution monitoring, traffic flow scheduling, etc. [0003] Commonly used time series data forecasting methods include statistical methods and machine learning methods. In recent years, with the continuous development of deep learning, Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) have shown good prediction performance in data prediction. However, commonly used methods cannot predict complex nonlinear time series well, and the performance of deep learning methods has improv...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26G06N3/04G06N3/08
CPCG06Q10/04G06Q50/26G06N3/08G06N3/044
Inventor 李丽娜黄盛奎李念峰靳德政
Owner CHANGCHUN UNIV
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