This is the first in a series of blog posts analysing and discussing Serbian quarterly transportation time series.
The aims of these blog posts are the following:
- To describe main features of quarterly transportation time series
- To model series using popular time series models
- To forecast future transportation time series values
- To compare forecasting performance of different time series models
The dataset under examination comes from the National Bank of Serbia database (http://nbs.rs/export/sites/default/internet/latinica/80/realni_sektor/SBRS03.xls). It shows two quarterly series: volume of services in road passenger and freight transport. Both series cover the same period: 2002Q1 to 2018Q2.
Column names in csv file are: Trapas (volume of services in road passenger transport, in million passenger kilometres) and Travol (volume of services in road freight transport, in million ton kilometres). Those familiar with R could download and use RData file. This file contains time series in different formats, but the time series used directly in the next blog posts are in the multivariate time series object Bts.name containing two above listed time series. This RData file contains also a few other time series objects in different format (a, Ats, Azoo, Azoo.name and Bts).
In the next blog post line graphs for each quarterly transportation time series will be presented and discussed.