{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":23,"n_cols":5,"rows_kept":23,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[17,2,17,3],[18,2,18,3],[20,2,20,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910544860003321"]
[null,null,"Autore",null,"Sangiorgio Matteo"]
[null,null,"Titolo",null,"Deep Learning in Multi-step Prediction of Chaotic Dynamics : From Deterministic Models to Real-World Systems / / by Matteo Sangiorgio, Fabio Dercole, Giorgio Guariso"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021"]
[null,null,"ISBN",null,"3-030-94482-4"]
[null,null,"Edizione",null,"[1st ed. 2021.]"]
[null,null,"Descrizione fisica",null,"1 online resource (111 pages)"]
[null,null,"Collana",null,"PoliMI SpringerBriefs, , 2282-2585"]
[null,null,"Disciplina",null,"003.857015118"]
[null,null,"Soggetti",null,"Neural networks (Computer science)"]
[{"$nulls":4},"Computational intelligence"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"System theory"]
[{"$nulls":4},"Mathematical Models of Cognitive Processes and Neural Networks"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Complex Systems"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Introduction to chaotic dynamics’ forecasting,. Basic concepts of chaos theory and nonlinear time-series analysis -- Artificial and real-world chaotic oscillators -- Neural approaches for time series forecasting -- Neural predictors’ accuracy -- Neural predictors’ sensitivity and robustness -- Concluding remarks on chaotic dynamics’ forecasting."]
[null,null,null,"Sommario/riassunto","The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from most of the current literature, it implements a multi-step approach, i.e., the forecast of an entire interval of future values. This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon. Going progressively from deterministic models with different degrees of complexity and chaoticity to noisy systems and then to real-world cases, the book compares the performances of various neural network architectures (feed-forward and recurrent). It also introduces an innovative and powerful approach for training recurrent structures specific for sequence-to-sequence tasks. The book also presents one "]
[{"$nulls":4},"of the first attempts in the context of environmental time series forecasting of applying transfer-learning techniques such as domain adaptation."]