Tesla Stock Price Forecast Based on ARIMA Model and Machine Learning Techniques
DOI:
https://doi.org/10.61173/pf98r920Keywords:
ARIMA model, machine learning techniques, Random Forest, stock price forecastingAbstract
One of the most significant aspects of the world economy is the stock market, and the forecasting of stock prices is gaining more and more significant attraction. Under the current international situation, Tesla’s stock price was selected as the research object, and Tesla stock’s open price from July 15, 2020, to July 15, 2024, was chosen as the data set. Auto-Regressive Integrated Moving Average (ARIMA) model and machine learning techniques were taken to forecast the stock price in this paper. Both methods give essentially correct predictions, but there are also some differences between those two methods. The ARIMA model provides statistical trends within a time range, while machine learning techniques provide a stock price prediction that is accurate to the day but has weaker interpretability. By combining the two methods people can get a more accurate forecast of the stock price and then make more informed decisions in the stock market.References
[1] Chen F. Analysis of key factors for tesla’s success. 2022 2nd International Conference on Enterprise Management and Economic Development (ICEMED 2022). Atlantis Press, 2022.
[2] Moritz T. R., Redlich P., Krenz S., Buxbaum-Conradi J. P., Wulfsberg J. P. Tesla Motors, Inc.: Pioneer towards a new strategic approach in the automobile industry along the open source movement?. 2015 Portland International Conference on Management of Engineering and Technology (PICMET). Portland, OR, USA, 2015: 85-92.
[3] Zhou H. Research on stock price prediction based on time series analysis. 2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence (DEAI2024). Hong Kong, China: ACM, 2024: 7 pages.
[4] Tsai C.-F., Wang S.-P. Stock price forecasting by hybrid machine learning techniques. Proceedings of the International MultiConference of Engineers and Computer Scientists 2009. Hong Kong, 2009, I, 18-20.
[5] Shahi T.B., Shrestha A., Neupane A., Guo W. Stock price forecasting with deep learning: a comparative study. Mathematics, 2020, 8(9): 1441.
[6] Sen J., Chaudhuri T. A robust predictive model for stock price forecasting. Proceedings of the 5th International Conference on Business Analytics and Intelligence (ICBAI 2017). Indian Institute of Management, Bangalore, INDIA, 2017: 11-13.
[7] Benvenuto D., Giovanetti M., Vassallo L., Angeletti S., Ciccozzi M. Application of the ARIMA model on the COVID-2019 epidemic dataset. Data Brief, 2020, 29: Article 105340.
[8] Yang C. Tesla stock price timeseries analysis and forecasting. Dang C.T., Cifuentes-Faura J., Li X. (eds). Proceedings of the 2nd International Conference on Business and Policy Studies. Singapore: Springer, 2023.
[9] Notation for ARIMA Models. Time series forecasting system. SAS Institute, 2015.
[10] Hyndman R. J., Athanasopoulos G. 8.9 Seasonal ARIMA models. 2015.
[11] Filder T.N., Muraya M.M., Mutwiri R.M. Application of seasonal autoregressive moving average models to analysis and forecasting of time series monthly rainfall patterns in embu county, kenya. Asian Journal of Probability and Statistics, 2019: 1-15.
[12] Ho T.K. Random decision forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition. Montreal, QC, 1995: 278-282.
[13] Ho T.K. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 832-844.
[14] Narimani R., Narimani A. A new hybrid model for improvement of ARIMA by DEA. Decision Science Letters, 2012, 1(2): 59-68.
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