A time series prediction model using constructive neural network

Accurate prediction of agricultural prices is beneficial to correctly guide the circulation of agricultural products and agricultural production and realize the equilibrium of supply and demand of agricultural area. On the basis of wavelet neural network, this paper, choosing tomato prices as study object, tomato retail price data from ten collection sites in Hebei province from January, 1st, 2013 to December, 30th, 2013 as samples, builds the tomato price time series prediction model to test price model. As the results show, model prediction error rate is less than 0.01, and the correlation (R2) of predicted value and actual value is 0.908, showing that the model could accurately predict tomatoes price movements. The establishment of the model will provide technical support for tomato market monitoring and early warning and references for related policies.

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Hydrological Time Series Prediction Model Based on Attention-LSTM Neural Network

Proceedings of the 2019 2nd International Conference on Machine Learning and Machine Intelligence - MLMI 2019 ◽

10.1145/3366750.3366756 ◽

2019 ◽

Author(s):

Yiran Li ◽

Juan Yang

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Time Series Prediction ◽

Model Based

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Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series

Energies ◽

10.3390/en12010161 ◽

2019 ◽

Vol 12 (1) ◽

pp. 161 ◽

Cited By ~ 12

Author(s):

Tianjun Zhang ◽

Shuang Song ◽

Shugang Li ◽

Li Ma ◽

Shaobo Pan ◽

...

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Coal Mine ◽

Time Series Prediction ◽

Mean Square ◽

Gas Concentration ◽

Concentration Time ◽

Concentration Time Series ◽

Concentration Prediction

Effective prediction of gas concentrations and reasonable development of corresponding safety measures have important guiding significance for improving coal mine safety management. In order to improve the accuracy of gas concentration prediction and enhance the applicability of the model, this paper proposes a long short-term memory (LSTM) cyclic neural network prediction method based on actual coal mine production monitoring data to select gas concentration time series with larger samples and longer time spans, including model structural design, model training, model prediction, and model optimization to implement the prediction algorithm. By using the minimum objective function as the optimization goal, the Adam optimization algorithm is used to continuously update the weight of the neural network, and the network layer and batch size are tuned to select the optimal one. The number of layers and batch size are used as parameters of the coal mine gas concentration prediction model. Finally, the optimized LSTM prediction model is called to predict the gas concentration in the next time period. The experiment proves the following: The LSTM gas concentration prediction model uses large data volume sample prediction, more accurate than the bidirectional recurrent neural network (BidirectionRNN) model and the gated recurrent unit (GRU) model. The average mean square error of the prediction model can be reduced to 0.003 and the predicted mean square error can be reduced to 0.015, which has higher reliability in gas concentration time series prediction. The prediction error range is 0.0005–0.04, which has better robustness in gas concentration time series prediction. When predicting the trend of gas concentration time series, the gas concentration at the time inflection point can be better predicted and the mean square error at the inflection point can be reduced to 0.014, which has higher applicability in gas concentration time series prediction.

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Time-series prediction model of PM2.5 concentration based on LSTM neural network

Journal of Physics Conference Series ◽

10.1088/1742-6596/1861/1/012055 ◽

2021 ◽

Vol 1861 (1) ◽

pp. 012055

Author(s):

Yiyang Li

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Time Series Prediction ◽

Pm2.5 Concentration

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Slope Displacement Prediction Model Based on LMD and BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.246-247.370 ◽

2012 ◽

Vol 246-247 ◽

pp. 370-376

Author(s):

Wei Yu ◽

Jing Lu Cai ◽

Feng Ping An

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Bp Neural Network ◽

Time Series Prediction ◽

High Accuracy ◽

Displacement Prediction ◽

Local Mean ◽

Slope Displacement

Slope displacement time series prediction model,a combination of Local mean decomposition(LMD) and BP neural network is presented.By selecting train samples on the basis of monitoring data on slope displacement and conducting an adaptive decomposing, several production function is obtained.After that, BP neural network is used to forecast the PF and finally adding it all up and the result is the predicton of slope displacement. BP neural network is used to optimize the parameters so as to improve the forecast accuracy.The model is put into application on the slope displacement forecasting of the permanent lock slope.The case study shows that the prediction result is of high accuracy, scientifically valid and has potential value in the field of slope displacement time series prediction.

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Financial Time Series Prediction Model Based Recurrent Neural Network

2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) ◽

10.1109/iccwamtip51612.2020.9317371 ◽

2020 ◽

Author(s):

Cheng Chaozhi ◽

Gao Yachun ◽

Jingwei Ni

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Recurrent Neural Network ◽

Financial Time Series ◽

Time Series Prediction ◽

Financial Time ◽

Model Based

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Neural Network Techniques for Time Series Prediction: A Review

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.3.3.281 ◽

2019 ◽

Vol 3 (3) ◽

Author(s):

Muhammad Faheem Mushtaq ◽

Urooj Akram ◽

Muhammad Aamir ◽

Haseeb Ali ◽

Muhammad Zulqarnain

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Time Series Data ◽

Weather Prediction ◽

Time Series Prediction ◽

Series Data ◽

Prediction Problem ◽

Neural Network Models ◽

Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series. Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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