scholarly journals Tourism Demand Forecasting Based on Grey Model and BP Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xing Ma
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Grey Model ◽  
Series Data ◽  
Forecasting Model ◽  
Data Series ◽  
Tourism Demand ◽  
Combination Model ◽  
Combination Forecasting ◽  
Domestic Tourism

This article aims to explore a more suitable prediction method for tourism complex environment, to improve the accuracy of tourism prediction results and to explore the development law of China’s domestic tourism so as to better serve the domestic tourism management and tourism decision-making. This study uses grey system theory, BP neural network theory, and the combination model method to model and forecast tourism demand. Firstly, the GM (1, 1) model is established based on the introduction of grey theory. The regular data series are obtained through the transformation of irregular data series, and the prediction model is established. Secondly, in the structure algorithm of the BP neural network, the BP neural network model is established using the data series of travel time and the number of people. Then, combining BP neural network with the grey model, the grey neural network combination model is established to forecast the number of tourists. The prediction accuracy of the model is analyzed by the actual time series data of the number of tourists. Finally, the experimental analysis shows that the combination forecasting makes full use of the information provided by each forecasting model and obtains the combination forecasting model and the best forecasting result so as to improve the forecasting accuracy and reliability.

A Two-Stage Combination Model for Wind Power Forecasting

2015 ◽  
Vol 737 ◽  
pp. 9-13
Author(s):  
Jun Zhang ◽  
Yuan Hao Wang ◽  
Ying Yi Li ◽  
Feng Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Wind Farm ◽  
General Trend ◽  
Two Stage ◽  
Combination Model ◽  
Second Stage ◽  
Combination Forecasting ◽  
The Mean ◽  
Power Sequence

With the wind farm data from the southeast coast this paper builds a two-stage combination forecasting model of output power based on data preprocessing which include filling up missing data and pre-decomposition. The first stage is a composite prediction of decomposed power sequence in which a time series and optimized BP neural network predict the general trend and the correlation of various factors respectively. The second stage is BP neural network with its input is the results of first stage. The effectiveness and accuracy of the two-stage combination model are verified by comparing the mean square error of the combination model and other models.

The Forecasting Research of Beijing Tourism Demand Based on the BP Neural Network

2014 ◽  
Vol 571-572 ◽  
pp. 128-131 ◽  
Author(s):  
Yang Yu ◽  
Shi Min Wang
Keyword(s):  
Neural Network ◽  
Empirical Research ◽  
Bp Neural Network ◽  
Forecasting Model ◽  
Tourism Demand ◽  
Basic Principles ◽  
The Future

This paper describes the basic principles and algorithm of the BP neural network and builds a forecasting model of Beijing tourism demand based on the BP neural network. The forecasting model can forecast and analyze the number of tourists in Beijing in the future, which using the MATLAB tools and the number of tourists in Beijing during 1994 to 2012 for empirical research. The results show that the forecasting model of Beijing tourism demand based on the BP neural network can forecast the number of tourists in Beijing in the future more accurately.

scholarly journals Container Throughput Forecasting of Tianjin-Hebei Port Group Based on Grey Combination Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chen He ◽  
Huipo Wang
Keyword(s):  
Neural Network ◽  
Information System ◽  
Bp Neural Network ◽  
Capacity Planning ◽  
Forecasting Model ◽  
Forecasting Accuracy ◽  
Combination Model ◽  
Comparison Results ◽  
Container Operation ◽  
Better Than

Container throughput forecasting plays an important role in port capacity planning and management. Regarding the issue of container throughput of Tianjin-Hebei Port Group, considering the container throughput is an incomplete grey information system affected by various factors, the effect is often unsatisfactory by adopting a single forecasting model. Therefore, this paper studies the issue by combining fractional GM (1, 1) and BP neural network. The comparison results show that the combination model performs better than other single models separately and has a higher level of forecasting accuracy. Furthermore, the combination model is adopted to forecast the container throughput of Tianjin-Hebei Port Group from 2021 to 2025, which would be a data reference for the future development optimization for the container operation of Tianjin-Hebei Port Group.

Adaptive combination forecasting model for China’s logistics freight volume based on an improved PSO-BP neural network

Kybernetes ◽  
2015 ◽  
Vol 44 (4) ◽  
pp. 646-666 ◽  
Author(s):  
Zhou Cheng ◽  
Tao Juncheng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecasting Model ◽  
Content Type ◽  
Forecasting Models ◽  
Combination Forecasting ◽  
Freight Volume ◽  
Convergence Performance ◽  
Combination Forecasting Model ◽  
Improved Pso

Purpose – To accurately forecast logistics freight volume plays a vital part in rational planning formulation for a country. The purpose of this paper is to contribute to developing a novel combination forecasting model to predict China’s logistics freight volume, in which an improved PSO-BP neural network is proposed to determine the combination weights. Design/methodology/approach – Since BP neural network has the ability of learning, storing, and recalling information that given by individual forecasting models, it is effective in determining the combination weights of combination forecasting model. First, an improved PSO based on simulated annealing method and space-time adjustment strategy (SAPSO) is proposed to solve out the connection weights of BP neural network, which overcomes the problems of local optimum traps, low precision and poor convergence during BP neural network training process. Then, a novel combination forecast model based on SAPSO-BP neural network is established. Findings – Simulation tests prove that the proposed SAPSO has better convergence performance and more stability. At the same time, combination forecasting models based on three types of BP neural networks are developed, which rank as SAPSO-BP, PSO-BP and BP in accordance with mean absolute percentage error (MAPE) and convergent speed. Also the proposed combination model based on SAPSO-BP shows its superiority, compared with some other combination weight assignment methods. Originality/value – SAPSO-BP neural network is an original contribution to the combination weight assignment methods of combination forecasting model, which has better convergence performance and more stability.

A Short-Term Combination Forecasting Model for Traffic Flow Based on the BP Neural Network

2012 ◽  
Vol 253-255 ◽  
pp. 1339-1344
Author(s):  
Tie Xin Cheng ◽  
Wen Bin Du ◽  
Jing Zhu Chen
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Bp Neural Network ◽  
Continuous Time ◽  
Nonlinear Problems ◽  
Forecasting Model ◽  
Short Term ◽  
Combination Forecasting ◽  
Combination Forecasting Model ◽  
Time Point

The forecasting for short-term traffic flow has always been one important and difficult research focus in the traffic forecasting areas. Based on the BP Neural Network, which was applied to nonlinear problems, the independent short-term forecasting models for the different traffic flow of the continuous time point series in one day and the constant date series at same time point were set up respectively, then, a short-term combination forecasting model for traffic flow, in which the regular fluctuations in the traffic flow data of the continuous time point series in one day and the constant date series at same time point were fully considered, was established, and can be applied to the complex spatio-temporal features of short-term traffic flow. With the sample of traffic flow dada, the forecasting results of the different models showed that the combination forecasting model provided a better forecast accuracy than the independent models.

scholarly journals An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

2019 ◽  
Vol 9 (7) ◽  
pp. 1487 ◽  
Author(s):  
Fei Mei ◽  
Qingliang Wu ◽  
Tian Shi ◽  
Jixiang Lu ◽  
Yi Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Load Forecasting ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Forecasting Model ◽  
Model Based ◽  
Net Load

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.

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