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

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...

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.

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