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Using ARIMA Model to Fit and Predict Index of Stock Price Based on Wavelet De-Noising
Shihua Luo,Fang Yan,Dejian Lai,Wenyi Wu,Fucai Lu 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.12
To accommodate non-stationarity and strong noise in the SPI data, the research used wavelet method for de-noising and autoregressive integrated moving average model(ARIMA) for prediction. Seven-day moving averages of closing time SPI data in four Asian stock marketswereanalyzed.Empiricalresults show that after de-noising more accurate forecasting results can be obtained in developed markets. More developed market indexes seem more significant improvement; while for less developed market indexes, the improvement of de-noising is less significant. This is in accordance with current situation of market.