Artificial Intelligence for Time Series Analysis
A.Y. 2026/2027
Learning objectives
The course aims to provide theoretical and practical skills for the automatic analysis of time series using statistical and artificial intelligence techniques.
Expected learning outcomes
Students will be able to i) model time series with a probabilistic approach, ii) design time series predictors, and iii) implement statistical and AI models in this context.
Lesson period: Third four month period
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Third four month period
Course syllabus
· Course structure, motivation, and examples of applications
· Overview of probability space and measure theory
· Random variables, stochastic processes, and stationarity
· Convergence of random variables and ergodic processes
· Maximum likelihood estimation (MLE) and maximum a posteriori (MAP)
· Maximum likelihood estimation (MLE) and maximum a posteriori (MAP) for linear and multiple linear regression
· Maximum likelihood estimation (MLE) and maximum a posteriori (MAP) for logistic regression
· World decomposition theorem, trends, and seasonality
· Linear stochastic models (MA, AR, and ARMA) and z-transform
· Optimal methods for time series forecasting and linear predictors
· Model identification for time series using adapted MLE/MAP and their validation
· Wiener filter and applications
· Kalman filter and applications
· Introduction to Markov chains and Hidden Markov Models
· Introduction to Deep Learning for time series: convolutional and recurrent neural networks
· Introduction to Generative AI for Time Series: sampling techniques and normalization flows
· Overview of probability space and measure theory
· Random variables, stochastic processes, and stationarity
· Convergence of random variables and ergodic processes
· Maximum likelihood estimation (MLE) and maximum a posteriori (MAP)
· Maximum likelihood estimation (MLE) and maximum a posteriori (MAP) for linear and multiple linear regression
· Maximum likelihood estimation (MLE) and maximum a posteriori (MAP) for logistic regression
· World decomposition theorem, trends, and seasonality
· Linear stochastic models (MA, AR, and ARMA) and z-transform
· Optimal methods for time series forecasting and linear predictors
· Model identification for time series using adapted MLE/MAP and their validation
· Wiener filter and applications
· Kalman filter and applications
· Introduction to Markov chains and Hidden Markov Models
· Introduction to Deep Learning for time series: convolutional and recurrent neural networks
· Introduction to Generative AI for Time Series: sampling techniques and normalization flows
Prerequisites for admission
Basic knowledge of statistics and probability (e.g. probability distribution, expected value, sample mean, etc.) and discrete mathematics (e.g. vectors, matrices, scalar product, etc.) are recommended.
Teaching methods
The course is structured in lectures of theory and exercises. Some lessons will be enriched by scientific seminars held by experts (based on their availability).
Teaching Resources
The material will be provided entirely by the professor in the form of slides, notes and code on myAriel.
Students interested may consult:
-Title: A first course in stochastic processes (2. ed), Authors: Karlin Samuel and Taylor Howard M., Year: 1975, Publisher: Academic press, ISBN: 01-239-8552-8, Compulsary book: no
Students interested may consult:
-Title: A first course in stochastic processes (2. ed), Authors: Karlin Samuel and Taylor Howard M., Year: 1975, Publisher: Academic press, ISBN: 01-239-8552-8, Compulsary book: no
Assessment methods and Criteria
The exam consists of a project on a specific topic chosen by the student and an oral exam. The oral exam will include some questions related to the program and a presentation of the project. The questions will weigh 1/3 of the final grade.
INFO-01/A - Informatics - University credits: 6
Lessons: 48 hours
Professor:
Rivolta Massimo Walter
Shifts:
Turno
Professor:
Rivolta Massimo WalterProfessor(s)