CNN-LSTM-VAE based time series trend prediction

Wei Li; Hui Gao; Zeqi Qin

doi:10.1117/12.3000935

9 August 2023 CNN-LSTM-VAE based time series trend prediction

Wei Li, Hui Gao, Zeqi Qin

Proceedings Volume 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023); 127820B (2023) https://doi.org/10.1117/12.3000935
Event: Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 2023, Kuala Lumpur, Malaysia

Abstract

In the context of mobile Internet, time series analysis has become an important way to capture the characteristics of data such as periodicity and correlation. Establishing a temporal sequence analysis model as an effective means to capture data features, for the problems of irregularity, nonlinearity, and inconspicuous feature relationships that commonly occur in sequences. In this paper, we use convolutional neural network to extract the potential features in the sequence, and combine the long and short term memory network to analyze the temporal features in the data; meanwhile, due to the "gate" structure of the long and short term memory network, some noise in the data is introduced into the model for training, resulting in the overfitting problem. -The decode-reconstruction network structure is used to remove this noise and improve the accuracy of the model. In this paper, we use the stock data of CBS as an example and compare it with the existing algorithm model, based on which we demonstrate the higher accuracy of this algorithm with different domain data sets.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wei Li, Hui Gao, and Zeqi Qin "CNN-LSTM-VAE based time series trend prediction", Proc. SPIE 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 127820B (9 August 2023); https://doi.org/10.1117/12.3000935

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