airport

A dataset containing the total number of monthly travellers recorded
in an unnamed airport between January 2013 and December 2019.

datasets

Fundamental time series forecasting models such as autoregressive integrated moving average (ARIMA), exponential smoothing, and simple moving average are included. For ARIMA models, the output follows the traditional parameterisation by Box and Jenkins (1970, ISBN: 0816210942, 9780816210947). Furthermore, there are functions for detailed time series exploration and decomposition, respectively. All data and result visualisations are generated by 'ggplot2' instead of conventional R graphical output. For more details regarding the theoretical background of the models see Hyndman, R.J. and Athanasopoulos, G. (2021) <https://otexts.com/fpp3/>.

Ka Karl Wu

tsforecast

Time Series Forecasting Functions

Ka Yui Karl Wu

airport function

A data frame with 84 rows and 2 variables:<dl>
 <dt>Date</dt>
<dd>Months in which the traveller numbers are recorded.</dd> <dt>Travellers</dt>
<dd>Total travellers in the airport of that month.</dd> <dt>AvgTemp</dt>
<dd>Monthly average temperature (in °C) of the city where the airport is located.</dd> <dt>AvgRain</dt>
<dd>Monthly average rain fall (in mm) in the city where the airport is located.</dd>
</dl>

Format

Airport Travellers Time Series. — airport

A data frame with 84 rows and 2 variables:<dl>
 <dt>Date</dt>
<dd>Months in which the traveller numbers are recorded.</dd>

 <dt>Travellers</dt>
<dd>Total travellers in the airport of that month.</dd>

 <dt>AvgTemp</dt>
<dd>Monthly average temperature (in °C) of the city where the airport is located.</dd>

 <dt>AvgRain</dt>
<dd>Monthly average rain fall (in mm) in the city where the airport is located.</dd>


</dl>

airport: Airport Travellers Time Series.

Description

Usage

Arguments

Format

Examples