Market Demand Prediction Model Based on Momentum Factor Optimization BP Algorithm

In recent years, the market demand for online car-hailing service has expanded dramatically. To satisfy the daily travel needs, it is important to predict the supply and demand of online car-hailing in an accurate manner, and make active scheduling based on the predicted gap between supply and demand. This paper puts forward a novel supply and demand prediction model for online carhailing, which combines the merits of convolutional neural network (CNN) and long short-term memory (LSTM). The proposed model was named convolutional LSTM (C-LSTM). Next, the original data on online car-hailing were processed, and the key features that affect the supply and demand prediction were extracted. After that, the C-LSTM was optimized by the AdaBound algorithm during the training process. Finally, the superiority of the C-LSTM in predicting online car-hailing supply and demand was proved through contrastive experiments.

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Software Defect Prediction Model Based on GA-BP Algorithm

Cyberspace Safety and Security - Lecture Notes in Computer Science ◽

10.1007/978-3-030-37352-8_13 ◽

2019 ◽

pp. 151-161

Author(s):

Mengtian Cui ◽

Yameng Huang ◽

Jing Luo

Keyword(s):

Prediction Model ◽

Bp Algorithm ◽

Defect Prediction ◽

Software Defect Prediction ◽

Model Based ◽

Software Defect

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Development of Residential Gas Demand Prediction Model Based on Deep Learning

KIISE Transactions on Computing Practices ◽

10.5626/ktcp.2019.25.6.303 ◽

2019 ◽

Vol 25 (6) ◽

pp. 303-307

Author(s):

Youngmin Kwon ◽

DaeHan Ahn ◽

Taejoon Park

Keyword(s):

Deep Learning ◽

Prediction Model ◽

Demand Prediction ◽

Model Based

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Tourist Demand Prediction Model Based on Improved Fruit Fly Algorithm

Security and Communication Networks ◽

10.1155/2021/3411797 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Chuangle Guo ◽

Wei Shang

Keyword(s):

Prediction Model ◽

Fruit Fly ◽

Fruit Fly Optimization Algorithm ◽

Optimization Strategy ◽

Fruit Fly Optimization ◽

Demand Prediction ◽

Model Based ◽

Change Trend ◽

Tourist Demand ◽

Robust Prediction

To accurately predict the development and change trend of the future, tourism market can effectively improve the planning and purpose of tourism development. In order to improve the accuracy of tourist demand prediction, this paper studies the tourist demand prediction model based on improved fruit fly algorithm. Aiming at the optimization defects of the traditional fruit fly optimization algorithm (FOA), the model introduces two concepts of sensitivity and pheromone, improves the optimization strategy and position replacement of fruit fly, improves the diversity of fruit fly population, modifies the global optimization characteristics of the algorithm, and improves the local search ability and search efficiency of the algorithm. By combining the improved AFOA with echo state network (ESN), a two-stage combined prediction model (AAFOA-ESN) is constructed. The experimental results show that the minimum prediction error accuracy of the model is only 0.55%, which has more robust prediction effect, faster convergence speed, and higher prediction accuracy.

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Energy big data demand prediction model based on fuzzy rough set

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189014 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5291-5300

Author(s):

Zhimei Duan ◽

Xiaojin Yuan ◽

Rongfei Zhu

Keyword(s):

Big Data ◽

Prediction Model ◽

Rough Set ◽

Energy Demand ◽

Energy Structure ◽

Attribute Importance ◽

Fuzzy Rough Set ◽

Demand Prediction ◽

Model Based ◽

Short Period

Energy is an indispensable material resource for human production and life. It is a powerful engine and an important guarantee for human survival, economic and social sustainable development and world change. The economy is developing rapidly, the demand for energy continues to grow, energy consumption has increased sharply in a short period, and the security of energy supply and demand has also shown a severe trend. Predicting energy demand is especially important. However, due to the many influencing factors and the lack of energy data, the energy demand prediction has great uncertainty in the prediction results. Because of the above problems, this paper proposes an energy big data demand prediction model based on a fuzzy rough set model. Firstly, according to the data, the factors affecting the energy demand are determined, and the fuzzy C-means clustering algorithm is used to discretize the data according to the characteristics of the fuzzy rough set. Then the decision table is established and the attribute importance is calculated, and then the neighborhood rough set is used for attribute reduction. Then extract the correlation rules to establish a prediction model. Compare the prediction model proposed in this paper with the existing gray prediction method and energy elasticity coefficient method. The results show that this method can more scientifically predict the changes in energy big data demand. Finally, based on the experimental results, the corresponding strategies for optimizing the energy structure are proposed to provide reference for the optimization and development of energy demand.

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