Zobrazeno 1 - 10
of 435
pro vyhledávání: '"Pino, Fernando"'
Time-series data in real-world settings typically exhibit long-range dependencies and are observed at non-uniform intervals. In these settings, traditional sequence-based recurrent models struggle. To overcome this, researchers often replace recurren
Externí odkaz:
http://arxiv.org/abs/2405.20799
Time-series data in real-world medical settings typically exhibit long-range dependencies and are observed at non-uniform intervals. In such contexts, traditional sequence-based recurrent models struggle. To overcome this, researchers replace recurre
Externí odkaz:
http://arxiv.org/abs/2403.10288
The best encoding is the one that is interpretable in nature. In this work, we introduce a novel model that incorporates an interpretable bottleneck-termed the Filter Bank (FB)-at the outset of a Variational Autoencoder (VAE). This arrangement compel
Externí odkaz:
http://arxiv.org/abs/2310.11940
One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a pas
Externí odkaz:
http://arxiv.org/abs/2306.05479
Sleep constitutes a key indicator of human health, performance, and quality of life. Sleep deprivation has long been related to the onset, development, and worsening of several mental and metabolic disorders, constituting an essential marker for prev
Externí odkaz:
http://arxiv.org/abs/2301.10156
Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time, comprising a tool that helps clinicians supervise patients' evolution over time and enhance the associated treatments' outcomes. Frequently, sleep
Externí odkaz:
http://arxiv.org/abs/2211.10371
Autor:
Moreno-Pino, Fernando, Zohren, Stefan
Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques based on machine learning can be employed when treating volatility as a univariate, daily time-series. Moreover
Externí odkaz:
http://arxiv.org/abs/2210.04797
We introduce PyHHMM, an object-oriented open-source Python implementation of Heterogeneous-Hidden Markov Models (HHMMs). In addition to HMM's basic core functionalities, such as different initialization algorithms and classical observations models, i
Externí odkaz:
http://arxiv.org/abs/2201.06968
Publikováno v:
In Food Chemistry 30 August 2024 450