ts_forecast {rcrimeanalysis} | R Documentation |
Time Series Forecast for Daily Crime Data
Description
This function transforms traditional crime data into a time series and forecasts future incident counts based on the input data over a specified duration. The forecast is computed using simple exponential smoothing with additive errors. Returned is a plot of the time series, trend, and the upper and lower prediction limits for the forecast.
Usage
ts_forecast(data, start, duration = NULL)
Arguments
data |
Data frame of crime or RMS data. See provided Chicago Data Portal example for reference |
start |
Start date for the time series being analyzed. The format is as follows: c('year', 'month', 'day'). See example below for reference. |
duration |
Number of days for the forecast. If |
Value
Returns a plot of the time series entered (black), a forecast over the specified duration (blue), the exponentially smoothed trend for both the input data (red) and forecast (orange), and the upper and lower bounds for the prediction interval (grey).
Author(s)
Jamie Spaulding, Keith Morris
Examples
#Using provided dataset from Chicago Data Portal:
data(crimes)
ts_forecast(crimes, start = c(2017, 1, 1))