Abstract
Predicting the value of stock prices is separate from external investment exposure to the stock market. The aim of the article is to evaluate selected time series forecasting models in forecasting stock data – exactly Apple Inc. (AAPL) stock price. There are not many articles in the literature on the possibility of using ML models in forecasting stock prices. It is important to ask the question whether ML models give better results than traditional models. To answer this question, in this article following regression models ARIMA, Logistic Regression were analyzed but also were used VEC, LSTM, XGBoost, Prophet.
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Ziółkowski, K. (2023). AAPL Forecasting Using Contemporary Time Series Models. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_27
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