Research on multi-dimensional target time series prediction model for power grid infrastructure projects

The prediction of future events based on available time series measurements is a relevant research area specifically for healthcare, such as prognostics and assessments of intervention applications. A measure of brain dynamics, electroencephalogram time series, are routinely analyzed to obtain information about current, as well as future, mental states, and to detect and diagnose diseases or environmental factors. Due to their chaotic nature, electroencephalogram time series require specialized techniques for effective prediction. The objective of this study was to introduce a hybrid system developed by artificial intelligence techniques to deal with electroencephalogram time series. Both artificial neural networks and the ant-lion optimizer, which is a recent intelligent optimization technique, were employed to comprehend the related system and perform some prediction applications over electroencephalogram time series. According to the obtained findings, the system can successfully predict the future states of target time series and it even outperforms some other hybrid artificial neural network-based systems and alternative time series prediction approaches from the literature.

Download Full-text

Tomato Prices Time Series Prediction Model Based on Wavelet Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.2636 ◽

2014 ◽

Vol 644-650 ◽

pp. 2636-2640 ◽

Cited By ~ 1

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.

Download Full-text

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

Download Full-text

Water quality management using statistical analysis and time-series prediction model

Applied Water Science ◽

10.1007/s13201-014-0159-9 ◽

2014 ◽

Vol 4 (4) ◽

pp. 425-434 ◽

Cited By ~ 37

Author(s):

Kulwinder Singh Parmar ◽

Rashmi Bhardwaj

Keyword(s):

Water Quality ◽

Time Series ◽

Statistical Analysis ◽

Quality Management ◽

Prediction Model ◽

Time Series Prediction ◽

Water Quality Management

Download Full-text

Based on Time Series Prediction of Photovoltaic Power Plant Output

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.5142 ◽

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.

Download Full-text