Deep Learning Models for Financial Time Series Forecasting in Investment Contexts - Hamid Wijaya; Lalu Puji Indra Kharisma; Yusuf Aliyu Madamu (Yayasan Sinergi Widya Nusantara (Sidyanusa), 2026)
Stocks represent an investment instrument that reflects ownership in a company, offering opportunities for profit through dividends or capital gains. However, stock prices often fluctuate due to various economic, social, and political factors, making price prediction a challenging task. This study applies the Long Short-Term Memory (LSTM) method, a deep learning architecture, to forecast the stock prices of Apple Inc. (AAPL) based on historical data obtained from Yahoo Finance covering the period from December 12, 1980, to September 20, 2024. The research process includes data cleaning, normalization using MinMaxScaler, data splitting with an 80:20 ratio for training and testing, and hyperparameter optimization through Grid Search. The optimal LSTM model configuration achieved 50 epochs, a batch size of 64, and a learning rate of 0.0001. Evaluation results demonstrate high accuracy, with a MAPE of 1.70%, RMSE of 3.030, and R-squared of 0.9975. Forecasts indicate a gradual decline in AAPL stock prices over the next 12 months, from 173.83 in October 2024 to 111.34 in September 2025. This research provides valuable insights for investors to better understand market dynamics and make informed investment decision.