A Comparative Analysis of ARIMA and VAR Algorithms for Performance Analysis of High-Speed Diesel Pumps
Keywords:
ARIMA, VAR, Time series forecasting, predictionAbstract
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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Bilal, M., Kim, H., Fayaz, M., Pawar, P.: Comparative analysis of time series forecasting approaches for household electricity consumption prediction. arXiv preprint arXiv:2207.01019 (2022)
Sufian, M.A., Varadarajan, J.: Enhancing prediction and analysis of uk road traffic accident severity using ai: Integration of machine learning, econometric techniques, and time series forecasting in public health research. arXiv preprint arXiv:2309.13483 (2023)
Wang, J., Shen, L.-z., Bi, Y., Lei, J.: Modeling and optimization of a light-duty diesel engine at high altitude with a support vector machine and a genetic algo- rithm. Fuel 285, 119137 (2021) https://doi.org/10.1016/J.FUEL.2020.119137
Prediction of diesel engine performance using support vector regression technique. Regular (2020) https://doi.org/10.35940/ijitee.j7494.0891020
Watson, D., Bostic, D., Harrison, T.V.: Comparative models of hydrocarbon emis- sions for a diesel engine operating at constant loads and speeds. Transactions of the ASABE 52, 1079–1087 (2009) https://doi.org/10.13031/2013.27778
Mahajan, S., Harikrishnan, R., Kotecha, K.: Prediction of network traffic in wire- less mesh networks using hybrid deep learning model. IEEE Access PP, 1–1 (2022) https://doi.org/10.1109/ACCESS.2022.3140646
Chato, L., Tayeb, S., Latifi, S.: A genetic algorithm to optimize the adaptive support vector regression model for forecasting the reliability of diesel engine systems. 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), 1–7 (2017) https://doi.org/10.1109/CCWC.2017.7868462
Fan, W.: Prediction and evaluation of sound quality of diesel engines based on neural network. Noise and Vibration Control (2014)
Tosun, E., Aydın, K., Bilgili, M.: Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures. alexandria engineering journal 55, 3081–3089 (2016) https://doi.org/10.1016/J.AEJ.2016.08.011
Qing-hua, F.: Regression analysis of the curves of the flow and lift when regulating the speed of centrifugal pumps. Chemical Engineering Machinery (2010)
A˘gbulut, , Gu¨rel, A., Sarıdemir, S.: Experimental investigation and prediction of performance and emission responses of a ci engine fuelled with different metal-oxide based nanoparticles–diesel blends using different machine learning algorithms. Energy 215, 119076 (2021) https://doi.org/10.1016/j.energy.2020. 119076
Moriyasu, R., Nojiri, S., Matsunaga, A., Nakamura, T., Jimbo, T.: Diesel engine air path control based on neural approximation of nonlinear mpc. Control Engi- neering Practice (2019) https://doi.org/10.1016/J.CONENGPRAC.2019.104114
Younis, Y., Abbas, S., Najim, F.T., Kamar, F.H., Nechifor, G.: Comparison of an artificial neural network and a multiple linear regression in predicting the heat of combustion of diesel fuel based on hydrocarbon groups. Revista De Chimie 71, 66–74 (2020) https://doi.org/10.37358/rc.20.6.8171
Zhang, R., Hu, J.: Production performance forecasting method based on mul- tivariate time series and vector autoregressive machine learning model for waterflooding reservoirs. Petroleum Exploration and Development 48(1), 201–211 (2021)
Katris, C.: Unemployment and covid-19 impact in greece: A vector autoregression (var) data analysis. Engineering Proceedings 5(1), 41 (2021)
Rajab, K., Kamalov, F., Cherukuri, A.K.: Forecasting covid-19: vector autoregression-based model. Arabian journal for science and engineering, 1–10 (2022)
Sethi, J.K., Mittal, M.: Analysis of air quality using univariate and multivariate time series models. In: 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 823–827 (2020). IEEE
Montiel Olea, J.L., Stock, J.H., Watson, M.W., et al.: Inference in struc- tural vector autoregressions identified with an external instrument. Journal of Econometrics 225(1), 74–87 (2021)
Adolf, J.K., Loossens, T., Tuerlinckx, F., Ceulemans, E.: Optimal sampling rates for reliable continuous-time first-order autoregressive and vector autoregressive modeling. Psychological Methods 26(6), 701 (2021)
Heaps, S.E.: Enforcing stationarity through the prior in vector autoregressions. Journal of Computational and Graphical Statistics 32(1), 74–83 (2023)
Ding, P., Jia, M., Yan, X.: Stationary subspaces-vector autoregressive with exoge- nous terms methodology for degradation trend estimation of rolling and slewing bearings. Mechanical Systems and Signal Processing 150, 107293 (2021)
Winarno, S., Usman, M., Kurniasari, D., et al.: Application of vector error cor- rection model (vecm) and impulse response function for daily stock prices. In: Journal of Physics: Conference Series, vol. 1751, p. 012016 (2021). IOP Publishing
Al-Qazzaz, R.A., Yousif, S.A.: High performance time series models using auto autoregressive integrated moving average. Indones. J. Electr. Eng. Comput. Sci 27, 422–430 (2022)
Elakkiya, E., Radha, M., Sathy, R.: Application of arima model for predicting cashew nut production in india–an analysis. Int. J. Res. Bus. Manage. 5, 45–52 (2017)
Mandrikova, O., Fetisova, N., Polozov, Y.: Hybrid model for time series of complex structure with arima components. Mathematics 9(10), 1122 (2021)
Unnikrishnan, P., Jothiprakash, V.: Hybrid ssa-arima-ann model for forecasting daily rainfall. Water Resources Management 34, 3609–3623 (2020)
Espasa, A., Pen´a, D.: The decomposition of forecast in seasonal arima models. Journal of Forecasting 14(7), 565–583 (1995)
Khetani, Vinit, et al. "Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains." International Journal of Intelligent Systems and Applications in Engineering 11.7s (2023): 253-262.
Mane, Deepak, et al. "Traffic Density Classification for Multiclass Vehicles Using Customized Convolutional Neural Network for Smart City." Communication and Intelligent Systems: Proceedings of ICCIS 2021. Singapore: Springer Nature Singapore, 2022. 1015-1030.
A. Bhujbal and D. Mane, “A survey on deep learning approaches for vehicle and number plate detection,” Int. J. Sci. Technol. Res., vol. 8, no. 12, pp. 1378–1383, 2019.
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