A study of the transaction volume prediction problem based on recurrent neural networks

Research Article
Open access

A study of the transaction volume prediction problem based on recurrent neural networks

Jingyu Hu 1*
  • 1 University of Washington    
  • *corresponding author Jhu2@uw.edu
Published on 26 December 2023 | https://doi.org/10.54254/2755-2721/29/20230778
ACE Vol.29
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-259-6
ISBN (Online): 978-1-83558-260-2

Abstract

With the rapid development of artificial intelligence technology, intelligent fintech scenarios based on big data are receiving more and more attention, and through the analysis of massive financial class data, accurate decision support can be provided for its various scenarios. By predicting the transaction volume of a financial product of a bank, abnormal transaction flow and gradual change trend can be found 1 day in advance to provide decision support for business department program development, and provide decision support for system expansion and contraction, thus reducing system online pressure or releasing unnecessary system resources. Linear algorithms such as AR model, MA model, ARMA model, etc. have poor prediction results for transaction volumes during holidays in the non-stationary dataset handled in this study due to strong assumptions on historical data. In this paper, we design and implement an LSTM-based trading volume prediction model LSTM-WP (LSTM-WebPredict) using deep learning algorithm, which can improve the accuracy of prediction of holiday trading volume by about 8% based on the linear algorithm by discovering and learning the features of historical data, and the learning ability of the model will gradually increase with the increasing of training data; Not only that, the research of this algorithm also provides corresponding technical accumulation for other business scenarios of time series problems, such as trend prediction and capacity assessment.

Keywords:

deep learning, recurrent neural networks, long short-term memory network, lstm-webpredict, feature engineering

Hu,J. (2023). A study of the transaction volume prediction problem based on recurrent neural networks. Applied and Computational Engineering,29,30-42.
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References

[1]. Dahl G E, Yu D, Deng L, et al. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(1): 30-42.

[2]. Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups[J]. IEEE Signal Processing Magazine, 2012, 29(6): 82-97.

[3]. Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.

[4]. Le Q V. Building high-level features using large scale unsupervised learning[C]//Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013: 8595-8598.

[5]. Collobert R, Weston J, Bottou L, et al. Natural language processing (almost) from scratch[J]. Journal of Machine Learning Research, 2011, 12(Aug): 2493-2537.

[6]. Ahmed A, Aly M, Gonzalez J, et al. Scalable inference in latent variable models[C]//International conference on Web search and data mining (WSDM). 2012, 51: 1257-1264.

[7]. Bank of China opens its banking portal [EB/OL]. http://open.boc.cn/

[8]. Recurrent neural network[EB/OL]. https://en.wikipedia.org/wiki/Recurrent_neural_network

[9]. Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep learning[EB/OL]. 2016

[10]. Hochreiter S, Schmidhuber J. Long short term memory[J]. Neural computation, 1997, 9(8): 1735-1780.

[11]. Understanding- Long Short Term Memory Network (LSTMs)[EB/OL]. http://colah.github.io/posts/2015-08-Understanding-LSTMs/

Cite this article

Hu,J. (2023). A study of the transaction volume prediction problem based on recurrent neural networks. Applied and Computational Engineering,29,30-42.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-259-6(Print) / 978-1-83558-260-2(Online)
Editor：Alan Wang, Marwan Omar, Roman Bauer
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.29
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Dahl G E, Yu D, Deng L, et al. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(1): 30-42.

[2]. Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups[J]. IEEE Signal Processing Magazine, 2012, 29(6): 82-97.

[3]. Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]//Advances in neural information processing systems. 2012: 1097-1105.

[4]. Le Q V. Building high-level features using large scale unsupervised learning[C]//Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013: 8595-8598.

[5]. Collobert R, Weston J, Bottou L, et al. Natural language processing (almost) from scratch[J]. Journal of Machine Learning Research, 2011, 12(Aug): 2493-2537.

[6]. Ahmed A, Aly M, Gonzalez J, et al. Scalable inference in latent variable models[C]//International conference on Web search and data mining (WSDM). 2012, 51: 1257-1264.

[7]. Bank of China opens its banking portal [EB/OL]. http://open.boc.cn/

[8]. Recurrent neural network[EB/OL]. https://en.wikipedia.org/wiki/Recurrent_neural_network

[9]. Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep learning[EB/OL]. 2016

[10]. Hochreiter S, Schmidhuber J. Long short term memory[J]. Neural computation, 1997, 9(8): 1735-1780.

[11]. Understanding- Long Short Term Memory Network (LSTMs)[EB/OL]. http://colah.github.io/posts/2015-08-Understanding-LSTMs/

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