Full exam for Model Identification and Data Analysis in the Computer Engineering degree programme at Politecnico di Milano. The document covers: 1 MODEL IDENTIFICATION AND DATA ANALYSIS – Module 1, A.Y. 2023/2024 Prof. Simone Formentin – September 11th, 2023 Surname Name University ID Number Signature ................................ ............................... .....................……… ………… .. ....................………
Full exam for Model Identification and Data Analysis in the Computer Engineering degree programme at Politecnico di Milano. The document covers: 1 MODEL IDENTIFICATION AND DATA ANALYSIS – Module 1, A.Y. 2023/2024 Prof. Simone Formentin – September 11th, 2023 Surname Name University ID Number Signature ................................ ............................... .....................……… ………… .. ....................………
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1 MODEL IDENTIFICATION AND DATA ANALYSIS – Module 1, A.Y. 2023/2024 Prof. Simone Formentin – September 11th, 2023 Surname Name University ID Number Signature ................................ ............................... .....................……… ………… .. ....................……… …………….. =========================================================================================================== - Write the solutions (including procedures and intermediate steps) in the blank areas (use the back of the page, if needed) - The number of pages is 4. Additional papers will not be considered. - Clarity, order and precision will be strongly considered for the final evaluation. =========================================================================================================== 1. [Stochastic processes - theory] Discuss the concept of a stationary stochastic process and then elaborate on how many distinct ways it can be equivalently represented. Answer. A stationary stochastic process is a crucial concept in time series analysis, characterized by the fact that its statistical properties remain invariant over time. Such processes can be equivalently represented in four distinct ways, each offering unique insights and applications: a. Mean and Covariance Function: in this representation, the mean provides information about the central tendency of the process, while the covariance function captures how data points at different time lags are related. This representation is particularly useful for understanding the process's statistical properties and for generating simulations. b. Mean and Spectral Density: the spectral density provides insights into the frequency domain characteristics of the process, allowing us to analyze its frequency components. It is especially valuable…
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