DocumentCode :
3391625
Title :
A new hybrid model for time-series prediction
Author :
Pan, Feng ; Xia, Min ; Bai, En Jian
Author_Institution :
Coll. of Inf. Sci. & Technol., Donghua Univ., Shanghai, China
fYear :
2009
fDate :
15-17 June 2009
Firstpage :
281
Lastpage :
286
Abstract :
This paper proposed a new hybrid model in order to increase time series prediction accuracy. This hybrid model considers the routine time prediction technique like AR, ANN or any others as atomic building block. A linear hybrid technique is used to combine their forecast result into the final result. The hybrid algorithm was tested against three different kinds of time series data. Experiments results showed the effectiveness of the proposed hybrid model.
Keywords :
autoregressive moving average processes; forecasting theory; time series; artificial neural nets; autoregression process; linear hybrid technique; time-series prediction; Accuracy; Artificial neural networks; Educational institutions; History; Information science; Neural networks; Paper technology; Predictive models; Smoothing methods; Testing; ANN; Autoregression; Hybrid; Prediction; Time series;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cognitive Informatics, 2009. ICCI '09. 8th IEEE International Conference on
Conference_Location :
Kowloon, Hong Kong
Print_ISBN :
978-1-4244-4642-1
Type :
conf
DOI :
10.1109/COGINF.2009.5250728
Filename :
5250728
Link To Document :
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