LSTM-based mining of change rules of consumption behavior characteristics

Yanzhi Pang; Xiang Wang; Shixuan Zhou; Jianqiu Chen; Guobin Gu; Xuguang Wen; Chun Bao; Shiyu Wang; Bote Liu; Xiali Cao

doi:10.1117/12.3040401

4 September 2024 LSTM-based mining of change rules of consumption behavior characteristics

Yanzhi Pang, Xiang Wang, Shixuan Zhou, Jianqiu Chen, Guobin Gu, Xuguang Wen, Chun Bao, Shiyu Wang, Bote Liu, Xiali Cao

Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132592T (2024) https://doi.org/10.1117/12.3040401
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China

Abstract

Our study is based on the Long Short-Term Memory recurrent neural network (LSTM) model, aiming at mining the changing rules of consumption behavior features. Through feature extraction and descriptive statistics on the consumption data of 5415 students over a three-year period, we reveal the degree of discrete and changing patterns of consumption behavior. The model construction part introduces the principle and framework of LSTM and its advantages in capturing long sequence dependencies. The algorithm implementation part demonstrates the effect of predicting the consumption behavior in the fourth year, and the accuracy and robustness of the model are verified by the RMSE values of the training set and test set as well as the fitting situation. The results of predicting fluctuations in consumption behavior characteristics, including total daily consumption, frequency of consumption, and average consumption amount, are further analyzed. The results of the study help to understand the dynamics of consumption behavior and provide reliable data support for the determination of poor students, which can help to help poor students get out of difficulties more accurately.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanzhi Pang, Xiang Wang, Shixuan Zhou, Jianqiu Chen, Guobin Gu, Xuguang Wen, Chun Bao, Shiyu Wang, Bote Liu, and Xiali Cao "LSTM-based mining of change rules of consumption behavior characteristics", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132592T (4 September 2024); https://doi.org/10.1117/12.3040401

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KEYWORDS

Data modeling

Mining

Education and training

Neural networks

Transportation

Feature extraction

Statistical analysis

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