Time Series Analysis: An application of ARIMA model in stock price forecasting
YiChen Dong, Siyi Li, Xueqin Gong
Available Online April 2017.
- https://doi.org/10.2991/iemss-17.2017.140How to use a DOI?
- ARIMA model, stock price prediction, time series analysis
- 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.
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 -