The Short-Term Forecast Model of Pork Price Based on CNN-GA

2012 ◽  
Vol 628 ◽  
pp. 350-358 ◽  
Author(s):  
Zhe Min Li ◽  
Shi Wei Xu ◽  
Li Guo Cui ◽  
Gan Qiong Li ◽  
Xiao Xia Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Basic Structure ◽  
Retail Price ◽  
Practical Significance ◽  
Short Term ◽  
The Neural Network ◽  
High Prediction ◽  
Short Term Forecasting

After analyzing and reviewing the short-term forecasting methods research of pork price at home and abroad, a chaotic neural network model based on genetic algorithm (CNN-GA) was put forward according to the nonlinear characteristics of pork price,which established on the base of the chaotic theory and the neural network technology. Chosen the daily retail price data of the pork (streaky pork) from January 1, 2008 to June 11, 2012,we designed the basic structure of CNN-GA, and thentrainedit in order to attain the trained CNN-GA model. Finally, the trained CNN-GA model was used to predict the 20 days’ (from June 12, 2012 to July 1, 2012) retail price of pork (streaky pork) and then compared the predicted price with the real price to test the model’s forecast accuracy and application ability.The result shows that the model has high prediction precision, good fitting effect and hasan important reference and practical significance for the short-term price forecasting of the pork market.

Neural Network Forecasting of Tourism Demand

2005 ◽  
Vol 11 (3) ◽  
pp. 301-328 ◽  
Author(s):  
Sen Cheong Kon ◽  
Lindsay W. Turner
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Accurate Method ◽  
Future Research ◽  
Data Series ◽  
Structural Method ◽  
Short Term ◽  
Neural Network Models ◽  
The Neural Network ◽  
Short Term Forecasting

In times of tourism uncertainty, practitioners need short-term forecasting methods. This study compares the forecasting accuracy of the basic structural method (BSM) and the neural network method to find the best structure for neural network models. Data for arrivals to Singapore are used to test the analysis while the naïve and Holt-Winters methods are used for base comparison of simpler models. The results confirm that the BSM remains a highly accurate method and that correctly structured neural models can outperform BSM and the simpler methods in the short term, and can also use short data series. These findings make neural methods significant candidates for future research.

SHORT-TERM TREND PREDICTION OF FOREIGN EXCHANGE RATES WITH A NEURAL-NETWORK BASED ENSEMBLE OF FINANCIAL TECHNICAL INDICATORS

2013 ◽  
Vol 22 (03) ◽  
pp. 1350016 ◽  
Author(s):  
THEODOROS ZAFEIRIOU ◽  
DIMITRIS KALLES
Keyword(s):  
Neural Network ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Short Term ◽  
Trend Prediction ◽  
Technical Indicators ◽  
The Neural Network ◽  
Exchange Markets ◽  
Short Term Forecasting ◽  
One Year

This paper is about designing, developing and training a neural network for short-term forecasting of buy-sell trends in foreign exchange markets. We use a set of established financial technical indicators as inputs to the neural network and we develop the architecture to predict a trend and then train the network based on the accuracy of the prediction. We perform extensive real time testing with the closing prices (one per minute) of the USD/EUR exchange rates for a one-year period. The overall approach delivers a system that predicts trends substantially better than individual technical indicators.

scholarly journals ASPECTS OF TEMPERATURE TAKING INTO ACCOUNT TO INCREASE THE ACCURACY OF SHORT-TERM FORECASTING OF NODE LOADS

2021 ◽  
Author(s):  
P. Shymaniuk ◽  
V Miroshnyk ◽  
I. Blinov ◽  
P. Chernenko
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Forecast Accuracy ◽  
Training Sample ◽  
Neural Network Training ◽  
Analysis Method ◽  
Short Term ◽  
Data Distortion ◽  
Network Training ◽  
Short Term Forecasting

The peculiarities of the influence of air temperature data on the accuracy of forecasting of nodal loads in power systems and how the accuracy of such forecasting changes depending on the training sample and its volume are considered. The application of the data analysis method to detect anomalous values ​​and omissions to reduce data distortion and improve forecasting results is considered. A neural network of deep learning of the LSTM type was used for multifactor prediction of nodal loads. To evaluate the effectiveness of the forecast accuracy, various variants of data samples for neural network training are considered.

Efficiency of Using Artificial Neural Network for Short-Term Load Forecasting

2015 ◽  
Vol 792 ◽  
pp. 312-316 ◽  
Author(s):  
Svetlana Rodygina ◽  
Valentina Lyubchenko ◽  
Alexander Rodygin
Keyword(s):  
Neural Network ◽  
Electricity Market ◽  
Load Forecasting ◽  
Operating Conditions ◽  
Short Term ◽  
The Neural Network ◽  
Short Term Load Forecasting ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Short Term Forecasting

Using artificial neural networks (ANN) for short-term load forecasting is an efficient method to get the best result. Considered problem of short-term load forecasting shows that the accuracy of short-term forecasting models and methods significantly influences on the further planning of operating conditions at the modern electricity market. The obtained error for short-term load forecasting using the neural network algorithm is 2.78%.

scholarly journals Ultra-Short-Term Forecasting of Photo-Voltaic Power via RBF Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1717
Author(s):  
Wanxing Ma ◽  
Zhimin Chen ◽  
Qing Zhu
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Smart Grids ◽  
Percentage Error ◽  
Solar Pv ◽  
Short Term ◽  
Forecasting Method ◽  
Photo Voltaic ◽  
Short Term Forecasting ◽  
Pv Power Generation

With the fast expansion of renewable energy systems during recent years, the stability and quality of smart grids using solar energy have been challenged because of the intermittency and fluctuations. Hence, forecasting photo-voltaic (PV) power generation is essential in facilitating planning and managing electricity generation and distribution. In this paper, the ultra-short-term forecasting method for solar PV power generation is investigated. Subsequently, we proposed a radial basis function (RBF)-based neural network. Additionally, to improve the network generalization ability and reduce the training time, the numbers of hidden layer neurons are limited. The input of neural network is selected as the one with higher Spearman correlation among the predicted power features. The data are normalized and the expansion parameter of RBF neurons are adjusted continuously in order to reduce the calculation errors and improve the forecasting accuracy. Numerous simulations are carried out to evaluate the performance of the proposed forecasting method. The mean absolute percentage error (MAPE) of the testing set is within 10%, which show that the power values of the following 15 min. can be predicted accurately. The simulation results verify that our method shows better performance than other existing works.

scholarly journals Short-term forecasting of soil temperature using artificial neural network

2014 ◽  
Vol 22 (3) ◽  
pp. 576-585 ◽  
Author(s):  
Hossein Tabari ◽  
P. Hosseinzadeh Talaee ◽  
Patrick Willems
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Temperature ◽  
Short Term ◽  
Artificial Neural ◽  
Short Term Forecasting

Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm

2015 ◽  
Vol 785 ◽  
pp. 14-18 ◽  
Author(s):  
Badar ul Islam ◽  
Zuhairi Baharudin ◽  
Perumal Nallagownden
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Forecast Accuracy ◽  
Back Propagation Neural Network ◽  
Search Performance ◽  
Training Algorithm ◽  
Short Term

Although, Back Propagation Neural Network are frequently implemented to forecast short-term electricity load, however, this training algorithm is criticized for its slow and improper convergence and poor generalization. There is a great need to explore the techniques that can overcome the above mentioned limitations to improve the forecast accuracy. In this paper, an improved BP neural network training algorithm is proposed that hybridizes simulated annealing and genetic algorithm (SA-GA). This hybrid approach leads to the integration of powerful local search capability of simulated annealing and near accurate global search performance of genetic algorithm. The proposed technique has shown better results in terms of load forecast accuracy and faster convergence. ISO New England data for the period of five years is employed to develop a case study that validates the efficacy of the proposed technique.

scholarly journals Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaobo Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Error Correction ◽  
Relative Error ◽  
Bp Neural Network ◽  
Arima Model ◽  
Forecast Model ◽  
Price Prediction ◽  
The Neural Network

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

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