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

Author

Listed:
  • Nazhifa Capella Az'Zahra
  • Widiarti Putri Kinasih
  • Syifana Hilmi Muthmainnah
  • Maghfira Azmi Aqila
  • Lutfiyah Nur Azizah

Abstract

This study compares the seasonal autoregressive integrated moving average (SARIMA) and support vector regression (SVR) methods for forecasting the stock prices of Pfizer Inc. and AstraZeneca Plc from 2017 to 2025. Monthly closing prices were obtained from Yahoo Finance and analysed in R Studio. SARIMA successfully captured the seasonal behaviour of the data, whereas SVR was applied to model potential non-linear patterns. Model performance was evaluated using mean absolute percentage error (MAPE) and root mean square error (RMSE). The results show that SARIMA achieved higher accuracy, with MAPE values of 4.97% for AstraZeneca and 4.95% for Pfizer, outperforming the SVR model in both MAPE and RMSE metrics. Forecasts generated using the SARIMA (0,1,1)(1,0,0)[12] model indicate a steady increase in AstraZeneca's stock price throughout 2026. In contrast, Pfizer's stock price is projected to remain stable at approximately $31 per share. Overall, the SARIMA model proved to be more effective in this study for predicting pharmaceutical stock prices characterised by seasonal patterns and moderate volatility.

Suggested Citation

  • Nazhifa Capella Az'Zahra & Widiarti Putri Kinasih & Syifana Hilmi Muthmainnah & Maghfira Azmi Aqila & Lutfiyah Nur Azizah, 2026. "Implementation of support vector regression and SARIMA for stock price prediction: Pfizer and AstraZeneca (2017-2025)," International Journal of Markets and Business Systems, Inderscience Enterprises Ltd, vol. 7(2), pages 138-162.
    • Handle: RePEc:ids:ijmabs:v:7:y:2026:i:2:p:138-162
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