Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Raffaele Mattera"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Autoencoders are dimension reduction models in the field of machine learning which can be thought of as a neural network counterpart of principal components analysis (PCA). Due to their flexibility and good performance, autoencoders have bee
Externí odkaz:
https://doaj.org/article/deee60bcf4754ce3a58508248fcfbaea
Publikováno v:
Quantitative Finance and Economics, Vol 7, Iss 3, Pp 491-507 (2023)
Assuming that stock prices follow a multi-fractional Brownian motion, we estimated a time-varying Hurst exponent ($ h_t $). The Hurst value can be considered a relative volatility measure and has been recently used to estimate market inefficiency. Th
Externí odkaz:
https://doaj.org/article/79918ae8fb8d4a48b9d4619c6384b161
Autor:
Raffaele Mattera, Maria Caterina Putti, Alessandra Biffi, Rosanna Parasole, Valentino Conter, Carmelo Rizzari, Maria Grazia Valsecchi, Daniela Silvestri, Andrea Biondi, Franco Locatelli, Elena Barisone, Concetta Micalizzi, Tommaso Mina, Valentina Kiren
Publikováno v:
HemaSphere, Vol 7, p e1005163 (2023)
Externí odkaz:
https://doaj.org/article/ff021e86dbe64d45ace6d546216fc8bf
Autor:
Giulio Mattera, Raffaele Mattera
Publikováno v:
Intelligent Systems with Applications, Vol 17, Iss , Pp 200181- (2023)
A large amount of assets characterizes high-dimensional portfolio selection problems compared to temporal observation. In such a high-dimensional framework, the asset allocation is unfeasible because the covariance matrix obtained with the usual samp
Externí odkaz:
https://doaj.org/article/b0f0039c511f4b0d8ae4caa1ce6933b2
Publikováno v:
Machine Learning with Applications, Vol 10, Iss , Pp 100417- (2022)
Although there are many contributions in the time series clustering literature, few studies still deal with count time series data. This paper aims to develop a fuzzy clustering procedure for count time series data. We propose an Integer GARCH-based
Externí odkaz:
https://doaj.org/article/22e4ad5da156491cbb4406dc25a1f62d
Many treatments are non-randomly assigned, continuous in nature, and exhibit heterogeneous effects even at identical treatment intensities. Taken together, these characteristics pose significant challenges for identifying causal effects, as no existi
Externí odkaz:
http://arxiv.org/abs/2409.08773
Publikováno v:
Complexity, Vol 2022 (2022)
Agent-based models are computational approaches used to reproduce the interactions between economic agents. These models are widely applied in many contexts to get deeper understanding about agents’ behaviors within complex systems. In this paper,
Externí odkaz:
https://doaj.org/article/c1341f0b3418494b8955764eed9d1e9e
Publikováno v:
Complexity, Vol 2022 (2022)
Market inefficiency is a latent concept, and it is difficult to be measured by means of a single indicator. In this paper, following both the adaptive market hypothesis (AMH) and the fractal market hypothesis (FMH), we develop a new time-varying meas
Externí odkaz:
https://doaj.org/article/3b75304de73a4cabb4ba9fe7b10b8a06
Autor:
Chiara Minotti, Daniele Mengato, Marica De Pieri, Sabrina Trivellato, Andrea Francavilla, Costanza Di Chiara, Cecilia Liberati, Raffaele Mattera, Alessandra Biffi, Carlo Giaquinto, Francesca Venturini, Daniele Donà
Publikováno v:
Viruses, Vol 15, Iss 1, p 192 (2023)
(1) Background: SARS-CoV-2 infection is notably mild in children, though comorbidities may increase the risk of hospitalization and may represent a risk for increased disease severity. There is an urgent need for targeted therapies with an acceptable
Externí odkaz:
https://doaj.org/article/c6c11e3ca757443982217b310de15c41
Publikováno v:
Symmetry, Vol 13, Iss 6, p 959 (2021)
The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate prope
Externí odkaz:
https://doaj.org/article/164ce3554f7e4a258515074823c826c8