Abstract. This work aims to improve an existing time series forecasting algorithm –LBF– by the application of frequent episodes techniques as a complementary step to the model....
— Rapidly evolving businesses generate massive amounts of time-stamped data sequences and defy a demand for massively multivariate time series analysis. For such data the predict...
— The paper proposes a hypernetwork-based method for stock market prediction through a binary time series problem. Hypernetworks are a random hypergraph structure of higher-order...
Elena Bautu, Sun Kim, Andrei Bautu, Henri Luchian,...
Abstract--In this paper, we introduce a novel approach to timeseries prediction realized both at the linguistic and numerical level. It exploits fuzzy cognitive maps (FCMs) along w...
Predicting the "Value at Risk" of a portfolio of stocks is of great significance in quantitative finance. We introduce a new class models, "dynamical products of ex...