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Evaluating a Seasonal ARIMA Model for Event Detection in New York City
Content Type: Abstract
ARIMA models use past values (autoregressive terms) and past forecasting errors (moving average terms) to generate future forecasts, making it a potential candidate method for modeling citywide time series of syndromic data [1]. While past research… read more -
Evaluation of Temporal Aberration Detection Methods in New York City Syndromic Data
Content Type: Abstract
The NYC syndromic surveillance system has been monitoring syndromes from NYC emergency department (ED) visits for over a decade. We applied several aberration detection methodologies to a time series of ED visits in NYC spiked with synthetic… read more