This paper presents a novel approach to the phase space reconstruction technique,
fractional-order phase space reconstruction (FOSS), which generalizes the traditional …

This paper introduces a novel statistical testing technique known as segmented detrended
multifractal fluctuation analysis (SMF‐DFA) to analyze the structured scaling properties of …

We construct a momentum factor that identifies cross-sectional winners and losers based on
a weighting scheme that incorporates all the price data, over the entire lookback period, as …

In the present work, a novel methodology for error detection in automatic weather stations
has been implemented. Time series acquired from two highly correlated stations with a …

In this paper, we investigate the necessary conditions to optimize a given functional,
involving a generalization of the tempered fractional derivative. The exponential function is …

Machine Learning (ML) for finance is a fruitful approach to detect patterns in data. However,
when it comes to predicting financial markets based on financial data with a low signal-to …

In the rapidly evolving dynamics of financial markets, predicting asset prices and currency
exchange rates remains a very complex issue. This is especially the case for the foreign …

The accurate identification of neuronal connectivity is an important task in neuroscience
research, as network interactions underlie the mechanisms that govern brain function and …

Given the unique topography of Quito, predicting climate change in this cityis challenging.
This thesis focuses on the study of meteorological data, specificallyfor the city of Quito. To …

Financial trading data presents a unique challenge for anomaly detection due to its high
dimensionality and often lack of labelled anomalous examples. Nevertheless, it is of great …