Proceedings of the 2017 International Conference on Innovations in Economic Management and Social Science (IEMSS 2017)

Time Series Analysis: An application of ARIMA model in stock price forecasting

Authors
YiChen Dong, Siyi Li, Xueqin Gong
Corresponding Author
YiChen Dong
Available Online April 2017.
DOI
https://doi.org/10.2991/iemss-17.2017.140How to use a DOI?
Keywords
ARIMA model, stock price prediction, time series analysis
Abstract
Time series models have been the foundation of the analysis of a process over a long period of time and their applications are manifold, including sales forecasting, index forecasting etc. In decisions involving uncertainties, time series models are noted as one of the most effective ways of making predictions. Among the many models, the autoregressive integrated moving average (ARIMA) models have been especially popular in time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA method.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - YiChen Dong
AU  - Siyi Li
AU  - Xueqin Gong
PY  - 2017/04
DA  - 2017/04
TI  - Time Series Analysis: An application of ARIMA model in stock price forecasting
BT  - Proceedings of the 2017 International Conference on Innovations in Economic Management and Social Science (IEMSS 2017)
PB  - Atlantis Press
SP  - 703
EP  - 710
SN  - 2352-5428
UR  - https://doi.org/10.2991/iemss-17.2017.140
DO  - https://doi.org/10.2991/iemss-17.2017.140
ID  - Dong2017/04
ER  -