A Comparative Analysis of ARIMA and VAR Algorithms for Performance Analysis of High-Speed Diesel Pumps


  • Smita Mahajan Artificial Intelligence and Machine Learning Department, Symbiosis Institute of Technology, (Pune campus), Lavale, Pune, 412115, Maharashtra, India.
  • Shivali Amit Wagle Artificial Intelligence and Machine Learning Department, Symbiosis Institute of Technology, (Pune campus), Lavale, Pune, 412115, Maharashtra, India.
  • Nihar Ranjan JSPM’S Rajarshi Shahu College of Engineering, Pune-411033, Maharashtra, India
  • Santosh Borde JSPM’S Rajarshi Shahu College of Engineering, Pune-411033, Maharashtra, India


ARIMA, VAR, Time series forecasting, prediction


The demand for precise and efficient forecasting of High-Speed Diesel (HSD)pump performance is critical for optimizing fuel distribution, operational planning, and resource allocation in the petroleum industry. This paper presents a comprehensive comparison analysis of implementing two widely used time series forecasting algorithms, Auto regressive Integrated Moving Average (ARIMA) and Vector Auto Regression (VAR), for predicting vibration in electrical systems. The study spans a year-long dataset collected at various intervals, including seconds, minutes, hours, days, weeks, months, and yearly intervals, leveraging data from voltage, current, and temperature sensors. The research analyzes "Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE)" as three critical indicators for evaluating how well ARIMA and VAR perform. The analysis reveals that ARIMA consistently outperforms VAR across all intervals, demonstrating superior accuracy in predicting vibration levels. The data The dataset collected from a range of sensors provides a diverse and rich source of information, effectively capturing the electrical system's dynamic behavior. The results highlight the significance of selecting an appropriate forecasting model for time series data, especially system reliability and maintenance applications. This research contributes to the ongoing discourse on algorithm selection in time series forecasting for electrical systems and provides valuable insights for practitioners and researchers alike.  The findings underscore the importance of considering the dataset's specific characteristics and the nature of the target variable when choosing between ARIMA and VAR algorithms for predictive modeling.


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How to Cite

Mahajan, S. ., Wagle, S. A. ., Ranjan, N. ., & Borde, S. . (2024). A Comparative Analysis of ARIMA and VAR Algorithms for Performance Analysis of High-Speed Diesel Pumps. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 01–13. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4832



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