A Hybrid Model of Differential Evolution with Neural Network on Lag Time Selection for Agricultural Price Time Series Forecasting

2017 ◽  
pp. 155-167
Author(s):  
Chen ZhiYuan ◽  
Le Dinh Van Khoa ◽  
Lee Soon Boon
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Differential Evolution ◽  
Hybrid Model ◽  
Lag Time ◽  
Time Series Forecasting ◽  
Selection For ◽  
Agricultural Price ◽  
Time Selection

scholarly journals Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1122
Author(s):  
Oksana Mandrikova ◽  
Nadezhda Fetisova ◽  
Yuriy Polozov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Complex Structure ◽  
Arima Model ◽  
Ionospheric Parameter ◽  
Arima Models ◽  
Complicated Structure ◽  
Threshold Functions ◽  
Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

An ARIMA-ANN Hybrid Model for Time Series Forecasting

2013 ◽  
Vol 30 (3) ◽  
pp. 244-259 ◽  
Author(s):  
Li Wang ◽  
Haofei Zou ◽  
Jia Su ◽  
Ling Li ◽  
Sohail Chaudhry
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Time Series Forecasting
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