Forecasting the Number of Firemen Interventions Using Exponential Smoothing Methods: A Case Study

Mallouhy, Roxane Elias; Guyeux, Christophe; Jaoude, Chady Abou; Makhoul, Abdallah

doi:10.1007/978-3-030-99584-3_50

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…In addition to the AR, MA, ARIMA and Prophet methods discussed above, there are time series prediction methods based on "exponential smoothing", which may be interesting to study in view of the seasonality of the intervention series. Experiments have shown [16] that the Holt-Winters method has the best accuracy compared to the classical time series prediction techniques previously studied and to other exponential smoothing methods.…”

Section: Time Series Approachmentioning

confidence: 98%

Predictions in Pre-Hospital Emergency Transport in France: A State of the Art

Guyeux¹

2022

Signal Processing and Vision

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For a number of years now, the regional fire department centers have been recording their interventions numerically. Such databases are under- utilized and are mainly used for statistical andmanagement purposes. However, such a history of interventions can be very useful, if used in conjunction with artificial intelligence algorithms, for predictive purposes. Such work has recently been done in France through a series of articles investigatingthe various aspects of the problem, and has been put into production at the Doubs center. The objective of this review is to take stock of all the workthat has been done so far, to list the successes andthe stumbling blocks, and to draw up a roadmap on this theme for the years to come.

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Section: Time Series Approachmentioning

confidence: 98%

Predictions in Pre-Hospital Emergency Transport in France: A State of the Art

Guyeux¹

2022

Signal Processing and Vision

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K-mean Clustering: a case study in Yvelines, Île-de-France

Mallouhy

Guyeux

Jaoude

et al. 2022

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)

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Analysis of Traffic Accidents and the Deployment of the Fire Rescue Service in the Czech Republic

KOLISKOVA,

NEUBAUER

2024

AD ALTA: Journal of Interdisciplinary Research

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Traffic accidents remain a topical safety issue. The aim of this article is to use the statistical apparatus to describe a random variable, the daily number of accidents, and to model it. The data on traffic accidents for the period 2012–2021, including time and location information, came from the database of the Fire Rescue Service of the Czech Republic. The study utilised an analysis of variance for statistical analysis with a count variable, following a Poisson distribution. One-factor and two-factor analyses are used to describe the dependence of the daily number of accidents involving the deployment of the fire rescue service on the day of the week, and month. The use of generalised linear model for count data to analyse the number of traffic accidents is unique in the Czech Republic.

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Forecasting the Number of Firemen Interventions Using Exponential Smoothing Methods: A Case Study

Cited by 4 publications

References 10 publications

Predictions in Pre-Hospital Emergency Transport in France: A State of the Art

Predictions in Pre-Hospital Emergency Transport in France: A State of the Art

K-mean Clustering: a case study in Yvelines, Île-de-France

Analysis of Traffic Accidents and the Deployment of the Fire Rescue Service in the Czech Republic

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