Modeling Temporal Data: A Comparison Between Time Series Methods and the Poisson Process

Ashraf Hassan Idris Brama

Abstract: This study aims to provide a comparative analysis between time series methods and the Poisson process in modeling temporal data. The comparison will focus on their theoretical foundations, assumptions, modeling capabilities, and forecasting performance using real temperatures for Khartoum State, Sudan. The findings of this study are expected to help researchers and practitioners choose the most suitable approach for their specific applications. It can be observed that indicate that forecasting temperatures in Khartoum State, Sudan using Box?Jenkins models yields an annual average of (31.61), whereas forecasting using a homogeneous Poisson process produces a constant average of (31.61) over the predicted years, this indicates that the homogeneous Poisson process can be used as an alternative to Box?Jenkins models in the forecasting process. This study offers useful material for both new and experienced researchers by providing guidance on time series Poisson process produces forecasting techniques and approaches that will help in enhancing the value of decision making.

Keywords: Poisson process, forecasting, Autocorrelation, Kolmogorov-Smirnov, stationary

How to Cite?: Ashraf Hassan Idris Brama, "Modeling Temporal Data: A Comparison Between Time Series Methods and the Poisson Process", Volume 15 Issue 9, September 2026, International Journal of Science and Research (IJSR), Pages: 1324-1328, https://www.ijsr.net/getabstract.php?paperid=SR26908170723, DOI: https://dx.doi.org/10.21275/SR26908170723

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