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20112022
most citedForecasting emergency medical service call arrival rates

124 citations · 189 across the 15 of their papers we have counts for

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Showing stat.APShow all

5 papers · 1 filter

stat.AP2021

Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects

Judy P. Che-Castaldo, Rémi Cousin, Stefani Daryanto +15

The electric power grid is a critical societal resource connecting multiple infrastructural domains such as agriculture, transportation, and manufacturing. The electrical grid as a…

stat.AP2020

Modeling a Nonlinear Biophysical Trend Followed by Long-Memory Equilibrium with Unknown Change Point

Wenyu Zhang, Maryclare Griffin, David S. Matteson

Measurements of many biological processes are characterized by an initial trend period followed by an equilibrium period. Scientists may wish to quantify features of the two period…

stat.AP201514 cited

Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach

Zhengyi Zhou, David S. Matteson

Predicting ambulance demand accurately at fine time and location scales is critical for ambulance fleet management and dynamic deployment. Large-scale datasets in this setting typi…

stat.AP2015

Predicting Melbourne Ambulance Demand using Kernel Warping

Zhengyi Zhou, David S. Matteson

Predicting ambulance demand accurately in fine resolutions in space and time is critical for ambulance fleet management and dynamic deployment. Typical challenges include data spar…

stat.AP2011124 cited

Forecasting emergency medical service call arrival rates

David S. Matteson, Mathew W. McLean, Dawn B. Woodard +1

We introduce a new method for forecasting emergency call arrival rates that combines integer-valued time series models with a dynamic latent factor structure. Covariate information…