ped_data

A dataset with hourly pedestrian counts at 43 locations in the
city Melbourne, australia, from 1 December, 2018 to 1, January, 2019.

datasets

This is a modification of 'HDoutliers' package. The 'HDoutliers' algorithm is a powerful
unsupervised algorithm for detecting anomalies in high-dimensional data, with a
strong theoretical foundation. However, it suffers from some limitations that
significantly hinder its performance level, under certain circumstances. This package
implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019)
<arXiv:1908.04000> for detecting anomalies in high-dimensional data
that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority
with a large distance gap. An approach based on extreme value theory is used
for the anomalous threshold calculation.

Priyanga Dilini Talagala

stray

Anomaly Detection in High Dimensional and Temporal Data

Rob J Hyndman

Kate Smith-Miles

ped_data function

A data frame with 33024 rows and 5 variables:<dl class="dl-horizontal">
<dt>Sensor </dt><dd>Sensor location</dd>
<dt>Date_Time </dt><dd>Time and date</dd>
<dt>Date</dt><dd>Date</dd>
<dt>Time</dt><dd>Time</dd>
<dt>Count</dt><dd>Pedestrian count</dd></dl>

Format

Dataset with pedestrian counts — ped_data

A data frame with 33024 rows and 5 variables:<dl class='dl-horizontal'>
<dt>Sensor </dt><dd>Sensor location</dd>
<dt>Date_Time </dt><dd>Time and date</dd>
<dt>Date</dt><dd>Date</dd>
<dt>Time</dt><dd>Time</dd>
<dt>Count</dt><dd>Pedestrian count</dd></dl>

ped_data: Dataset with pedestrian counts

Description

Usage

Arguments

Format