Time series prediction model for sequential learning

2007 ◽  
Vol 90 (12) ◽  
pp. 129-139
Author(s):  
Manabu Gouko ◽  
Yoshihiro Sugaya ◽  
Hirotomo Aso
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Time Series Prediction ◽  
Sequential Learning

Tomato Prices Time Series Prediction Model Based on Wavelet Neural Network

2014 ◽  
Vol 644-650 ◽  
pp. 2636-2640 ◽  
Author(s):  
Jian Hua Zhang ◽  
Fan Tao Kong ◽  
Jian Zhai Wu ◽  
Meng Shuai Zhu ◽  
Ke Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Agricultural Production ◽  
Supply And Demand ◽  
Time Series Prediction ◽  
Wavelet Neural Network ◽  
Agricultural Products ◽  
Price Model ◽  
Agricultural Prices

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.

Based on Time Series Prediction of Photovoltaic Power Plant Output

2011 ◽  
Vol 383-390 ◽  
pp. 5142-5147
Author(s):  
Wei Guo Li ◽  
Zhi Min Liao ◽  
Xue Lin Sun
Keyword(s):  
Time Series ◽  
Power Systems ◽  
Prediction Model ◽  
Time Series Prediction ◽  
System Capacity ◽  
Stable Operation ◽  
Power Prediction ◽  
Pv System ◽  
Test And Evaluation ◽  
System Output

With the PV power system capacity continues to expand, PV power generation forecasting techniques can reduce the PV system output power of randomness, it has great impact on power systems. This paper presents a method based on ARMA time series power prediction model. With historical electricity data and meteorological factors, the model gets test and evaluation by Eviews software. Results indicated that the prediction model has high accuracy, it can solve the shortcomings of PV randomness and also can improve the ability of the stable operation of the system.

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

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.

Sign in / Sign up

Export Citation Format

Share Document