Hydrological Time Series Prediction Model Based on Attention-LSTM 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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NARMAX-MODEL-BASED TIME SERIES PREDICTION: FEEDFORWARD AND RECURRENT FUZZY NEURAL NETWORK APPROACHES

Applied Computational Intelligence ◽

10.1142/9789812702661_0097 ◽

2004 ◽

Cited By ~ 1

Author(s):

Y. GAO ◽

M. J. ER ◽

J. DU

Keyword(s):

Neural Network ◽

Time Series ◽

Fuzzy Neural Network ◽

Time Series Prediction ◽

Model Based ◽

Fuzzy Neural ◽

Network Approaches ◽

Recurrent Fuzzy Neural Network

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Traffic Accident Time Series Prediction Model Based on Combination of ARIMA and BP and SVM

Journal of Traffic and Logistics Engineering ◽

10.18178/jtle.7.2.41-46 ◽

2019 ◽

pp. 41-46

Author(s):

Xiaorui Shao ◽

◽

Lester L Boey ◽

Yifei Luo

Keyword(s):

Time Series ◽

Prediction Model ◽

Traffic Accident ◽

Time Series Prediction ◽

Model Based

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Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method

Mathematical Problems in Engineering ◽

10.1155/2021/6613604 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Guorong Zhu ◽

Sha Peng ◽

Yongchang Lao ◽

Qichao Su ◽

Qiujie Sun

Keyword(s):

Time Series ◽

Power Consumption ◽

Prediction Model ◽

Time Series Prediction ◽

Electricity Consumption ◽

Operating Efficiency ◽

Short Term ◽

Model Based ◽

Consumption Data ◽

Consumption Prediction

Short-term electricity consumption data reflects the operating efficiency of grid companies, and accurate forecasting of electricity consumption helps to achieve refined electricity consumption planning and improve transmission and distribution transportation efficiency. In view of the fact that the power consumption data is nonstationary, nonlinear, and greatly influenced by the season, holidays, and other factors, this paper adopts a time-series prediction model based on the EMD-Fbprophet-LSTM method to make short-term power consumption prediction for an enterprise's daily power consumption data. The EMD model was used to decompose the time series into a multisong intrinsic mode function (IMF) and a residual component, and then the Fbprophet method was used to predict the IMF component. The LSTM model is used to predict the short-term electricity consumption, and finally the prediction value of the combined model is measured based on the weights of the single Fbprophet and LSTM models. Compared with the single time-series prediction model, the time-series prediction model based on the EMD-Fbprophet-LSTM method has higher prediction accuracy and can effectively improve the accuracy of short-term regional electricity consumption prediction.

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