Prediction of univariate financial series
Between econometric and connectionist approaches
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Time series prediction has been the subject of a considerable number of studies due to the innumerable amounts of temporal and sequential data produced daily by the information industry and various research structures. This field has undergone a spectacular effervescence and has continued to grow in recent years with the explosion of digital data, Big Data and especially artificial intelligence. This book represents a technical introduction to the different methods of predicting univariate chronicles on financial markets with empirical applications, while mobilizing two families of completely ...
Time series prediction has been the subject of a considerable number of studies due to the innumerable amounts of temporal and sequential data produced daily by the information industry and various research structures. This field has undergone a spectacular effervescence and has continued to grow in recent years with the explosion of digital data, Big Data and especially artificial intelligence. This book represents a technical introduction to the different methods of predicting univariate chronicles on financial markets with empirical applications, while mobilizing two families of completely distinct approaches, a first one based on econometric models and a second one based on machine learning by recurrent artificial neural networks.
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