Statistical and Machine Learning models for Time Series Analysis

Scuola Normale Superiore
LEVEL
PhD
TYPE
Course
MODES
-
LANGUAGE
-
ECTS
6
PERIOD
01/03/2024 to 23/05/2024

Course Description

Introduction. Components of a time series (trend, cycle, seasonal, irregular), stationarity, autocorrelation and dependencies, approaches to time series analysis. Review of estimation methods (Least Squares, Maximum Likelihood, Generalized Method of Moments). Linear models. ARMA processes, partial autocorrelation, invertibility, ARIMA models for non-stationary series. Inference of linear models: identification and fitting, diagnostics, Ljung-Box statistic model selection. Vector AutoRegressive models, reduced form, structural form e identification issues. Granger causality. State space models. Filtering, prediction and smoothing Kalman recursions local level models. Particle filtering and smoothing, Score Driven models, Hidden Markov Models. Neural networks for time series. Introduction to (Deep) Neural Networks, Inference of time series models with Machine Learning methods. Overview of time series forecasting via ML and Deep Learning Libraries: TensorFlow, Keras. Recurrent Neural Networks (RNN), Gated Architectures (LSTMs, GRUs), Bi-directional RNNs, Deep RNN. Reservoir computing and Echo State Networks. Applications and examples. Introduction to Reinforcement Learning.

Subject area

Digital communications IA electronics
Mathematics

Field area

Digital
Industry and Space

Time format

weekly

Educational-info

Prerequisites

at least a Bachelor

Duration

45

Day of the weeks

TBD

ECTS

6

Validation mode

Oral Examination

Maximum number of students

15

Organizer

Partner

Scuola Normale Superiore

Faculty

Scuola Normale Superiore

Department

Classe di Scienze

Contact or registration links