airLDN

A dataset that contains daily observations of individual air pollutants
from 2014 to 2020.

datasets

The nonparametric trend and its derivatives in equidistant time
series (TS) with long-memory errors can be estimated. The
estimation is conducted via local polynomial regression using an
automatically selected bandwidth obtained by a built-in iterative plug-in
algorithm or a bandwidth fixed by the user.
The smoothing methods of the package are described in Letmathe, S., Beran,
J. and Feng, Y., (2023) <doi:10.1080/03610926.2023.2276049>.

Dominik Schulz

esemifar

Smoothing Long-Memory Time Series

Yuanhua Feng

Jan Beran

Sebastian Letmathe

airLDN function

A data frame with 2552 rows and 8 variables:<dl>
 <dt>date</dt>
<dd>the observation date</dd> <dt>pm25</dt>
<dd>particle matter with an aerodynamic diameter smaller than 2.5
 \(\mu\)m</dd> <dt>pm10</dt>
<dd>particle matter with an aerodynamic diameter smaller than 10
 \(\mu\)m</dd> <dt>o3</dt>
<dd>ozone or trioxygen</dd> <dt>no2</dt>
<dd>nitrogen dioxide</dd> <dt>so2</dt>
<dd>sulphur dioxide</dd> <dt>co</dt>
<dd>carbon monoxide</dd> <dt>AQI</dt>
<dd>composite air quality index</dd>
</dl>

Format

Daily Observations of the Air Quality Index of London (Britain) — airLDN

A data frame with 2552 rows and 8 variables:<dl>
 <dt>date</dt>
<dd>the observation date</dd>

 <dt>pm25</dt>
<dd>particle matter with an aerodynamic diameter smaller than 2.5
 \(\mu\)m</dd>

 <dt>pm10</dt>
<dd>particle matter with an aerodynamic diameter smaller than 10
 \(\mu\)m</dd>

 <dt>o3</dt>
<dd>ozone or trioxygen</dd>

 <dt>no2</dt>
<dd>nitrogen dioxide</dd>

 <dt>so2</dt>
<dd>sulphur dioxide</dd>

 <dt>co</dt>
<dd>carbon monoxide</dd>

 <dt>AQI</dt>
<dd>composite air quality index</dd>


</dl>

airLDN: Daily Observations of the Air Quality Index of London (Britain)

Description

Usage

Arguments

Format