Time Series and Mobility Data Analysis

Big Data Mining
Academic Year: 

The purpose of the course is to introduce the main techniques in data mining and machine learning (including deep learning approaches) for the analysis of temporal data, in particular for time series and spatio-temporal data related to human mobility. The presentation will be supported by several case studies developed with the SoBigData.eu Laboratory.


Time Series Analysis, Mobility data types; spatio-temporal trajectories; routines and mobility profiles; trajectory patterns and trajectory models.

Technics and tools: 

The methods and techniques presented include the following: trajectory and profile reconstruction methods; O/D matrices of the mobility demand; trajectory clustering; flock-, swarm- and convoy- patterns; mobile activity recognition. In the course, the M-Atlas platform will be introduced, for the analysis of mobile objects' trajectories.

Case studies and datasets: 

Sample case studies will be presented, on: study of urban mobility through GPS data; study of presence and inter-city fluxes through mobile phone data (application of Big Data for Official Statistics); traffic planning on national scale through mobile phone data; public transport studies (performances and integration with carpooling) based on GPS and planned public transport service. Exercises will involve (also) public GPS datasets (taxi) and/or social media data (Flickr).


Knowledge of the main opportunities, issues and limitations in the analysis of Bog Mobility Data; knowledge of the main data mining methods for such data; skills to use some specific analysis tools, developed over real datasets.