Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 3rd International Conference on Business and Policy Studies
Series Vol. 71 , 18 January 2024
Open
Access | Article
Explore the Impact of Initial Data Coherence on ARIMA Model Prediction Based on Python
* Author to whom correspondence should be addressed.
Abstract
As the most intuitive economic indicator reflecting the economic development level of a company, an industry, or even a country, the fluctuation of stock prices will attract great attention. In order to avoid the loss of investors to a certain extent, stock price prediction is widely used in the stock market to prevent risks to a certain extent, and the ARIMA model is a time series prediction model that has been widely used in recent years. Therefore, the application of the ARIMA model to stock price forecasting is the main research object of this paper. This paper speculated at the beginning of the study that there may be a close relationship between the degree of fit of the ARIMA model and the consistency of the prediction results with the original data used. However, the existing literature is basically blank in this aspect of research and analysis. Therefore, this paper chooses to conduct comparative experiments on a set of continuous data and a set of discontinuous data to analyze the influence of the consistency of the original data under application conditions on the ARIMA model fitting and prediction results, hoping to fill the gaps in the existing literature through this research. The experimental results show that in some cases of original data with obvious discontinuous data missing, its smoothness is extremely poor, resulting in a poor fitting effect of the ARIMA model, and it can not be applied to the prediction of such data.
Keywords
ARIMA, Stock price, Stock prediction, Stationarity, ADF
References
1. King, B. F. (1966). Market and industry factors in stock price behavior. the Journal of Business, 39(1), 139-190.
2. Lucas, D. J., & McDonald, R. L. (1990). Equity issues and stock price dynamics. The journal of finance, 45(4), 1019-1043.
3. Shumway, R. H., Stoffer, D. S., Shumway, R. H., & Stoffer, D. S. (2017). ARIMA models. Time series analysis and its applications: with R examples, pp.75-163.
4. Ho, S. L., & Xie, M. (1998). The use of ARIMA models for reliability forecasting and analysis. Computers & industrial engineering, 35(1-2), 213-216.
5. Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014, March). Stock price prediction using the ARIMA model. In 2014 UKSim-AMSS 16th international conference on computer modelling and simulation, IEEE, pp. 106-112.
6. Mondal, P., Shit, L., & Goswami, S. (2014). Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. International Journal of Computer Science, Engineering and Applications, 4(2), 13.
7. Lopez, J. H. (1997). The power of the ADF test. Economics Letters, 57(1), 5-10.
8. Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52(3), 345-370.
9. Vrieze, S. I. (2012). Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychological methods, 17(2), 228.
10. Demir, V., Zontul, M., & Yelmen, I. (2020, September). Drug sales prediction with ACF and PACF supported ARIMA method. In 2020 5th International Conference on Computer Science and Engineering (UBMK), IEEE, pp. 243-247.
11. Piccolo, D. (1990). A distance measure for classifying ARIMA models. Journal of time series analysis, 11(2), 153-164.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).
- Volume Title
- Proceedings of the 3rd International Conference on Business and Policy Studies
- ISBN (Print)
- 978-1-83558-281-7
- ISBN (Online)
- 978-1-83558-282-4
- Published Date
- 18 January 2024
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/71/20241386
- Copyright
- 18 January 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited