A novel ConvLSTM with multifeature fusion for financial intelligent trading

Kong, Xiangming; Luo, Chao

doi:10.1002/int.22971

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…Motivation. Time-series forecasting (TSF) is a recent dynamic technique for managing CPS, intelligent processing, fnancial analysis [1], and equipment fault diagnosis [2,3]. Hrabia et al [4] pointed out that if an efective analysis model and prediction of COVID-19 can be developed, the epidemic prevention policy can be adjusted accordingly.…”

Section: Research Motivation and Problem Statementmentioning

confidence: 99%

Hybrid Time-Series Prediction Method Based on Entropy Fusion Feature

Zhang,

Yang,

Feng

2023

International Journal of Intelligent Systems

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High-precision time sequence forecasting is a complicated cyber-physical system (CPS) task. Due to the diversity of data scales and types, the classic time-series prediction model meets the challenge to deliver accurate prediction results for many forms of time-series data. This work proposes a hybrid model with long short-term memory (LSTM) and embedded empirical mode decomposition (EEMD) based on the entropy fusion feature. First, we apply EEMD in entropy fusion feature long short-term memory (ELSTM) to lessen pattern confusion and edge effects in traditional empirical mode decomposition (EMD). The sequence is then divided into intrinsic mode functions (IMF) by using EEMD. Then, feature vectors are constructed between IMFs and their respective information entropy for feature merging. LSTM is used to build a full connection network for each entropy fusion feature IMF subsequence for prediction and each type of IMF subsequence as the feature dimension to obtain its prediction results. Finally, the output results of all IMF subsequences are reconstructed to obtain the final prediction result. Compared with the LSTM method, the performance of the proposed method has been improved 64.33% on the evaluation metric MAPE. The proposed model has also delivered the best prediction outcomes across four different time-series datasets. The experimental results conclusively show that the proposed method outperforms other models compared.

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Section: Research Motivation and Problem Statementmentioning

confidence: 99%

Hybrid Time-Series Prediction Method Based on Entropy Fusion Feature

Zhang,

Yang,

Feng

2023

International Journal of Intelligent Systems

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“…On the whole, the personalized recommendation of financial services combined with artificial intelligence technology breaks the traditional passive service mode. It can make full use of the advantages of various resources to proactively carry out a full range of financial services for the purpose of meeting the personalized needs of users [18][19]. The key to the success of customized recommendation of financial services lies in the active provision of financial products that truly meet the needs of users, effectively implementing the concept of financial services centered on user needs [20].…”

Section: Introductionmentioning

confidence: 99%

Application of AI technology in personalized recommendation system for financial services

Yue

2024

Applied Mathematics and Nonlinear Sciences

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How to quickly and accurately retrieve the products that users are interested in from a huge amount of financial products has become a business pain point that financial institutions must solve. In this study, an interpretable EPRSA model for personalized financial service recommendations based on self-attention mechanisms is proposed by combining an LSTM model and an LDA topic model with AI technology support. A customized recommendation system for financial services is constructed by introducing the Nginx server into the Flask framework, and the design of the database and personalized recommendation module is interpreted. For the financial service personalized recommendation system proposed in this paper, its recommendation performance and system performance are tested, and the stock financial products are selected as the recommendation objects to explore its recommendation effect. It is found that the DNCG index of personalized recommendation of financial products of the EPRSA model is improved by 40.18%, the average response time of the system when the number of concurrent users is 1,000 is 1.96 s. The average quality of the personalized recommendation of the collection of stocks reaches 0.153. The customized recommendation of financial services using AI technology can select financial products based on the investor’s preference, help investors better understand the product returns, and improve the service quality of the financial industry.

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Performance prediction in online academic course: a deep learning approach with time series imaging

Ben Said,

Abdel-Salam,

Hazaa

2023

Multimed Tools Appl

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With the COVID-19 outbreak, schools and universities have massively adopted online learning to ensure the continuation of the learning process. However, in such setting, instructors lack efficient mechanisms to evaluate the learning gains and get insights about difficulties learners encounter. In this research work, we tackle the problem of predicting learner performance in online learning using a deep learning-based approach. Our proposed solution allows stakeholders involved in the online learning to anticipate the learner outcome ahead of the final assessment hence offering the opportunity for proactive measures to assist the learners. We propose a two-pathway deep learning model to classify learner performance using their interaction during the online sessions in the form of clickstreams. We also propose to transform these time series of clicks into images using the Gramian Angular Field. The learning model makes use of the available extra demographic and assessment information. We evaluate our approach on the Open University Learning Analytics Dataset. Comprehensive comparative study is conducted with evaluation against state-of-art approaches under different experimental settings. We also demonstrate the importance of including extra demographic and assessment data in the prediction process.

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A novel ConvLSTM with multifeature fusion for financial intelligent trading

Abstract: High fluctuation and self-similarity are typical characteristics of financial time series. Furthermore, affected by market environment, such as regular announcement of important economic data, time/

Cited by 5 publications

References 34 publications

Hybrid Time-Series Prediction Method Based on Entropy Fusion Feature

Hybrid Time-Series Prediction Method Based on Entropy Fusion Feature

Application of AI technology in personalized recommendation system for financial services

Performance prediction in online academic course: a deep learning approach with time series imaging

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