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Predicting Call Volume Using Coevolutionary Phenomena and Stochastic Models
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AI
DOI：10.1007/978-3-032-02312-4_29.png)
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Abstract
En 中文
In this article, we propose a noval predictive apprach for prehospital emergency services (PES) demand. The model leverages a negative binomial regression approach, designed to capture the co-evolution of influencing phenomena, including weather conditions, temporal patterns and previous calls volume. To enhance predictive accuracy, we conducted a detailed analysis of these phenomena, examining their impact on call volume variations. The model is validated using real-world data from the Laurentides and Lanaudiere regions in Quebec, achieving superior performance compared to existing methods such as Poisson regression and Multi -Layer Perceptron (MLP) neural networks. This work demonstrates that emergency call volumes are highly predictable when meteorological factors are effectively integrated into forecasting models.
Keywords:
PES demand prediction
coevolution time series
phenomena analysis
negative binomial regression
meteorological data
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