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Course Details Context Academic Year 2024/2025 Name Dott. - MI (1380) Ingegneria dell'Informazione / Information Technology Programme Year 1 Course Details ID Code 062785 Course Title TIME SERIES ANALYSIS Course Type MONO-DISCIPLINARY COURSE Credits (CFU / ECTS) 5.0 Course Description "The course is organized in 6 lectures. Lecture 1 (5h) Introduction to time series analysis Definition of time series data Main applications of time series analysis Statistical vs dynamical models perspective Components of a time series Additive vs Multiplicative models Time series decomposition approaches Stationarity in time series Definition of stationarity in time series; Autocorrelation Weak vs strong stationarity Notable examples of stationary and nonstationary time series How to identify and determine stationarity Transformation to make time series stationary Smoothing Smoothing in time series data The mean squared error Simple average technique Equally weighted moving average Exponentially weighted moving average Single, double, and triple exponential smoothing Lecture 2 (4h) AR-MA The autocorrelation function The partial autocorrelation function The Auto-Regressive model The Moving-Average model ARMA, ARIMA, SARIMA Autoregressive Moving Average (ARMA) models Autoregressive Integrated Moving Average (ARIMA) models SARIMA models (ARIMA model for data with seasonality) Automatic model selection Manual model selection Lecture 3 (4h) Unit root test and Hurst exponent Unit root test Mean Reversion Hurst Exponent Geometric Brownian Motion Application to financial time series Kalman filter Introduction to Kalman Filter Model components and assumptions The Kalman Filter algorithm Application to static and dynamic one-dimensional data Application to higher-dimensional data Lecture 4 (4h) Signal transforms and filters Introduction to Fourier Transform and Discrete Fourier Transform Fourier Transform of common signals, the main properties of FT Properties of the Fourier Transform Signal filtering Low-pass, high-pass, band-pass, and bass-stop filters Application of Fourier Transform for time series forecasting Prophet Introduction to Prophet for time series forecasting The model's components: trend, seasonality, and holidays How to use the prophet library in Python to perform time series forecasting Advanced options and configurations to compute predictions notebook, solution Lecture 5 (3h) Neural networks and Reservoir Computing Windowed approaches and Neural Networks for time series forecasting Forecasting with the Multi Layer Perceptron Recurrent Neural Networks: advantages and challenges Reservoir Computing and the Echo State Network Dimensionality reduction with Principal Component Analysis Forecasting electricity consumption with Multi Layer Perceptron and Echo State Network Lecture 6 (5h) Non-linear time series analysis Dynamical systems and nonlinear dynamics Bifurcation diagrams Chaotic systems Higher-dimensional continuous-time systems Phase space Fractal dimensions Phase space reconstruction and Taken's embedding theorem Forecasting nonlinear time series Time series classification and clustering Multivariate time series Time series similarity Dynamic Time Warping Time series embedding Classification of time series Clustering of time series " Scientific-Disciplinary Sector (SSD) SSD Code SSD Description CFU ING-INF/05 INFORMATION PROCESSING SYSTEMS 5.0 Details Alphabetical group Name Teaching Assignment Details From (included) To (excluded) A ZZZZ Roveri Manuel, Bianchi Filippo Maria

manifestidott v. 1.12.2 / 1.12.2

Area Servizi ICT

16/11/2025