Implementation of support vector regression and SARIMA for stock price prediction: Pfizer and AstraZeneca (2017-2025)

Key Points

• This research aims to forecast stock prices using advanced modeling techniques for specific pharmaceutical companies.
• Applied support vector regression and SARIMA models for prediction.
• Analyzed stock prices of Pfizer and AstraZeneca from 2017 to 2025.
• Conducted time series analysis to evaluate model performance.
• Identified effective algorithms for predicting stock prices.
• Demonstrated varying predictive accuracy between the two companies.
• Showed potential for improved forecasting in financial markets.