Credits:
3
Hours:
30
Area:
Big Data Mining
Teachers:
Academic Year:
2023-2024
Description:
The course will deal with time series and spatio-temporal data, in particular mobility. We will illustrate the fundamental characteristics of these two data classes as well as the most common pre-processing and analysis methods. Finally, each lesson will provide examples of use and exercises carried out in Python with the appropriate libraries.
Prerequisites: Data Mining & Machine Learning, Python
Notions:
- Lesson 1: Time Series: characteristics and similarity measures
- Lesson 2: Time Series: patterns (Motifs, Discords, Sequential patterns)
- Lesson 3: Time Series: forecasting
- Lesson 4: Introduction to Geospatial Analytics and fundamental concepts
- Lesson 5: Geospatial and Mobility data preprocessing and semantic enrichment
- Lesson 6: Individual & Collective mobility laws and models
- Lesson 7: Mobility Patterns and Location prediction
Technics and tools:
- scikit-mobility
- pandas
- tslearn
Competences:
- Conoscenza delle caratteristiche fondamentali di varie sorgenti di dati per time series e mobilità
- Conoscenza di metodi analitici di base (predittivi, clustering e pattern) per time series e dati di mobilità
- Capacità di realizzazione di semplici processi analitici in python per time series e dati di mobilità
