nyc_taxi

Number of NYC taxi passengers, where the five anomalies occur during the NYC
marathon, Thanksgiving, Christmas, New Years day, and a snow storm. The raw
data is from the NYC Taxi and Limousine Commission. The data file included
here consists of aggregating the total number of taxi passengers into 30
minute buckets.

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

nyc_taxi 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

nyc_taxi. — nyc_taxi

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>

nyc_taxi: nyc_taxi.

Description

Usage

Arguments

Format