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

DOI

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.

Copyright

© 2017, 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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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  - 10.2991/iemss-17.2017.140
ID  - Dong2017/04
ER  -