Regimes Identification and Data Compression: Problems and Applications Roy Cerqueti 0 1 2 Declaration on Generative AI Department of Social and Economic Sciences, Sapienza University of Rome , Piazzale Aldo Moro, 5, Rome 00185 , Italy GRANEM - UniversiteĢ d'Angers , 49036 Angers, CEDEX 01 , France

Data Science is grounded on the remarkable availability of data. Such an abundance leads to unavoidable concerns related to managing large datasets. This talk enters this debate by providing some remarks on identifying regimes in the temporal datasets and exploring their stochastic structure. On this, we also discuss data compression frameworks, with special attention to the competing targets of maintaining the features of the original sample and pursuing a simplification of the considered sample. Models, applied contexts, and open problems will be highlighted. The author has not employed any Generative AI tools.