TravelTime_451

Real time traffic data from the Twin Cities Metro area in Minnesota, collected
by the Minnesota Department of Transportation. Included metrics include
occupancy, speed, and travel time from specific sensors.

datasets

Implements a set of online fault detectors for time-series, called: PEWMA see M. Carter
et al. (2012) <doi:10.1109/SSP.2012.6319708>, SD-EWMA and TSSD-EWMA see H. Raza et al.
(2015) <doi:10.1016/j.patcog.2014.07.028>, KNN-CAD see E. Burnaev et al. (2016)
<arXiv:1608.04585>, KNN-LDCD see V. Ishimtsev et al. (2017) <arXiv:1706.03412> and
CAD-OSE see M. Smirnov (2018) <https://github.com/smirmik/CAD>. The first three
algorithms belong to prediction-based techniques and the last three belong to
window-based techniques. In addition, the SD-EWMA and PEWMA algorithms are algorithms
designed to work in stationary environments, while the other four
are algorithms designed to work in non-stationary environments.

Alai<c3><b1>e Iturria

otsad

Online Time Series Anomaly Detectors

Jacinto Carrasco

Francisco Herrera

Santiago Charramendieta

Karmele Intxausti

TravelTime_451 function

A data frame with three variables: <code>timestamp</code>, <code>value</code>,
 <code>is.real.anomaly</code>.
For further details, see <a href="https://github.com/numenta/NAB/blob/master/data/README.md">https://github.com/numenta/NAB/blob/master/data/README.md</a>

Format

TravelTime_451. — TravelTime_451

A data frame with three variables: <code>timestamp</code>, <code>value</code>,
 <code>is.real.anomaly</code>.
For further details, see <a href='https://github.com/numenta/NAB/blob/master/data/README.md'>https://github.com/numenta/NAB/blob/master/data/README.md</a>

TravelTime_451: TravelTime_451.

Description

Usage

Arguments

Format