This study proposes a novel approach for modeling financial time series, concentrating on
data pre-processing and selecting effective features in conventional and proposed modeling …

Time series forecasting has become a critical task due to its high practicality in real-world
applications such as traffic, energy consumption, economics and finance, and disease …

Abstract We introduce a Gaussian Process (GP) generalization of ResNets (with unknown
functions of the network replaced by GPs and identified via MAP estimation), which includes …

Rising car ownership rates lead to increased congestion, especially in inner-city areas,
which consequently results in a deterioration of the quality of life for residents. Although it is …

For various target tracking applications, it is well known that the Kalman filter is the optimal
estimator (in the minimum mean-square sense) to predict and estimate the state (position …

This paper explores entity matching and its vital role in e-commerce to track products across
different domains. Focusing on five diverse approaches, we evaluate their performance …

This paper explores entity matching and its vital role in e-commerce to track products across
different domains. Focusing on five diverse approaches, we evaluate their performance …

Large variations in stock price and unstable fluctuations will not only bring huge losses but
also might influence the decisions made by investors. Machine learning in financial areas …

For various target tracking applications, it is well known that the Kalman filter is the optimal
estimator (in the minimum mean-square sense) to predict and estimate the state (position …