Proceedings of the 2015 International Conference on Education, Management, Information and Medicine

Stock Index Forecasting Based on Hybrid ARIMA and LSSVM Methodology

Authors
Lei Yuan
Corresponding Author
Lei Yuan
Available Online April 2015.
DOI
10.2991/emim-15.2015.135How to use a DOI?
Keywords
ARIMA; LSSVM; Hybrid model; Time series forecasting; Stock index
Abstract

Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving the forecasting accuracy of time series, especially when there is a big difference between the combined models. Autoregressive Integrated Moving Average (ARIMA) model is one of the most popular linear models for time series forecasting. However, ARIMA model cannot effectively capture nonlinear patterns hidden in a time series. As a nonlinear model, Least Squares Support Vector Machine (LSSVM) can be applied to time series forecasting with a high degree of accuracy. Combining ARIMA model and LSSVM may further improve the prediction performance. It can helps investors making investment decisions to forecast stock index effectively. In this paper, a hybridization of ARIMA and LSSVM is proposed to forecast the daily closing price of SSE 180 stock index. The empirical results indicate that when linear and nonlinear models were hybridized properly, the forecasting performance of the hybrid model proposed in this paper outperforms the ARIMA model, LSSVM model and other hybrid models.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 International Conference on Education, Management, Information and Medicine
Series
Advances in Economics, Business and Management Research
Publication Date
April 2015
ISBN
978-94-62520-68-4
ISSN
2352-5428
DOI
10.2991/emim-15.2015.135How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Lei Yuan
PY  - 2015/04
DA  - 2015/04
TI  - Stock Index Forecasting Based on Hybrid ARIMA and LSSVM Methodology
BT  - Proceedings of the 2015 International Conference on Education, Management, Information and Medicine
PB  - Atlantis Press
SP  - 679
EP  - 684
SN  - 2352-5428
UR  - https://doi.org/10.2991/emim-15.2015.135
DO  - 10.2991/emim-15.2015.135
ID  - Yuan2015/04
ER  -