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SELF-PACED course: Approaches to analysing time-series data


Tourist destination managers, service providers, researchers want to better understand traffic flows: what impact traffic flows have, when do traffic peaks occur, what are the seasonal effects, which areas are more / less congested with tourists, how the weather, holidays and other events affect this behaviour. With the help of data analysis and forecasts, they can prepare for periods of increased visits, adjust marketing activities, service offerings or resources allocation. Today, the study of tourist mobility is largely based on surveys, physical observation and guesswork based on past experience.

In this tutorial we are going to look at a real world dataset consisting of traffic counters on Slovenian roads, provided by the Slovenian Road Network (DARS), and its application on to the mentioned questions.

The tutorial will show how to conduct basic Exploratory Data Analysis, data cleaning, feature engineering as well as some machine learning techniques for predicting traffic like Decision Trees and Markov State Models.

Recording and material

Follow the link to access Recording and Material.

Remark: This is a self-paced course (also available as a repository, therefore the provided start-end time are fictitious. The material is always available.

Presenters: Tomislav Šubić (ARCTUR)
Online: Online event by ARCTUR


Free entry.



Arctur d.o.o.