Sales Demand Prediction Model of Gated Recurrent Unit Neural Network Based on Improved Shape Distance Loss Function

2021 ◽  
Author(s):  
Haichuan Lou ◽  
Zhiwei Zhang ◽  
Baihui Zha
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Loss Function ◽  
Demand Prediction ◽  
Shape Distance ◽  
Gated Recurrent Unit

scholarly journals Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network

2020 ◽  
Vol 8 (4) ◽  
pp. 249 ◽  
Author(s):  
Zhen Zhang ◽  
Xinliang Pan ◽  
Tao Jiang ◽  
Baikai Sui ◽  
Chenxi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Short Term Memory ◽  
Absolute Error ◽  
Sea Surface ◽  
Neural Network Algorithm ◽  
Stability And Accuracy ◽  
Gated Recurrent Unit

The sea surface temperature (SST) is an important parameter of the energy balance on the Earth’s surface. SST prediction is crucial to marine production, marine protection, and climate prediction. However, the current SST prediction model still has low precision and poor stability. In this study, a medium and long-term SST prediction model is designed on the basis of the gated recurrent unit (GRU) neural network algorithm. This model captures the SST time regularity by using the GRU layer and outputs the predicted results through the fully connected layer. The Bohai Sea, which is characterized by a large annual temperature difference, is selected as the study area, and the SSTs on different time scales (monthly and quarterly) are used to verify the practicability and stability of the model. The results show that the designed SST prediction model can efficiently fit the results of the real sea surface temperature, and the correlation coefficient is above 0.98. Regardless of whether monthly or quarterly data are used, the proposed network model performs better than long short-term memory in terms of stability and accuracy when the length of the prediction increases. The root mean square error and mean absolute error of the predicted SST are mostly within 0–2.5 °C.

scholarly journals Use of Appropriate Loss Function in Rainfall Prediction using Deep Learning

2020 ◽  
Vol 9 (6) ◽  
pp. 293-297
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Loss Function ◽  
Historical Data ◽  
Daily Rainfall ◽  
Cross Entropy ◽  
Learning Approach ◽  
Rainfall Prediction

India is an agricultural country, and rainfall is the main source of irrigation for agriculture. Prediction of rainfall is very crucial for farmers to make decisions. In this research paper, the prediction model has been developed through deep learning using historical data of 10 years of rainfall. A deep learning approach used Keras API with an artificial neural network technique to predict the daily rainfall. The prediction model has been assessed by four-loss function, i.e., MSE, MAE, Hinge, and Binary Cross-Entropy.

scholarly journals A Convolutional Long Short-Term Memory Neural Network Based Prediction Model

2020 ◽  
Vol 15 (5) ◽  
Author(s):  
Yonghong Tian ◽  
Qi Wu ◽  
Yue Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Supply And Demand ◽  
Original Data ◽  
Market Demand ◽  
Short Term ◽  
Term Memory ◽  
Demand Prediction ◽  
Long Short Term Memory

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.

Study on Natural Gas Demand Prediction Model in China

2013 ◽  
Vol 869-870 ◽  
pp. 533-536
Author(s):  
Xue Feng ◽  
Jin Suo Zhang ◽  
Shao Hui Zou ◽  
Wuyunbilige Bao
Keyword(s):  
Neural Network ◽  
Natural Gas ◽  
Prediction Model ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Arima Model ◽  
Combined Model ◽  
Demand Prediction ◽  
Natural Gas Demand ◽  
Accuracy And Stability

Based on the characteristics of natural gas demand trend, this paper proposed ARIMA model which can predict China's natural gas demand as an effective tool. Compared with the RBF neural network model and combined model, empirical results show that the accuracy and stability of the ARIMA model is best.

Stock Market Prediction using Data Mining Techniques

2020 ◽  
pp. 310-313
Author(s):  
Karunesh Makker ◽  
Prince Patel ◽  
Hrishikesh Roy ◽  
Sonali Borse
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Prediction Model ◽  
Efficient Market Hypothesis ◽  
Deterministic System ◽  
Vast Number ◽  
Factors Influencing ◽  
Number Of Factors ◽  
Using Data ◽  
Robust Prediction

Stock market is a very volatile in-deterministic system with vast number of factors influencing the direction of trend on varying scales and multiple layers. Efficient Market Hypothesis (EMH) states that the market is unbeatable. This makes predicting the uptrend or downtrend a very challenging task. This research aims to combine multiple existing techniques into a much more robust prediction model which can handle various scenarios in which investment can be beneficial. Existing techniques like sentiment analysis or neural network techniques can be too narrow in their approach and can lead to erroneous outcomes for varying scenarios. By combing both techniques, this prediction model can provide more accurate and flexible recommendations. Embedding Technical indicators will guide the investor to minimize the risk and reap better returns.

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