218 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Jan Długosz UniversityPL40 papers
- Hamburg University of TechnologyDE17 papers
- Saarland UniversityDE15 papers
- Universität HamburgDE13 papers
- Lviv Polytechnic National UniversityUA11 papers
- Institute of MathematicsPL6 papers
- Centre National de la Recherche ScientifiqueFR5 papers
- Deutsches Elektronen-Synchrotron DESYDE5 papers
- Jiangsu Normal UniversityCN5 papers
- Military University of Technology in WarsawPL5 papers
- Barcelona Supercomputing CenterES4 papers
- Federal Institute For Materials Research and TestingDE4 papers
6 papers · 1 filter
Multivariate Forecasting of Bitcoin Volatility with Gradient Boosting: Deterministic, Probabilistic, and Feature Importance Perspectives
Grzegorz Dudek, Mateusz Kasprzyk, Paweł Pełka
This study investigates the application of the Light Gradient Boosting Machine (LGBM) model for both deterministic and probabilistic forecasting of Bitcoin realized volatility. Uti…
Forecasting Cryptocurrency Prices using Contextual ES-adRNN with Exogenous Variables
Slawek Smyl, Grzegorz Dudek, Paweł Pełka
In this paper, we introduce a new approach to multivariate forecasting cryptocurrency prices using a hybrid contextual model combining exponential smoothing (ES) and recurrent neur…
Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality
Grzegorz Dudek
In this paper, we investigate meta-learning for combining forecasts generated by models of different types. While typical approaches for combining forecasts involve simple averagin…
Ensembles of Randomized NNs for Pattern-based Time Series Forecasting
Grzegorz Dudek, Paweł Pełka
In this work, we propose an ensemble forecasting approach based on randomized neural networks. Improved randomized learning streamlines the fitting abilities of individual learners…
Autoencoder based Randomized Learning of Feedforward Neural Networks for Regression
Grzegorz Dudek
Feedforward neural networks are widely used as universal predictive models to fit data distribution. Common gradient-based learning, however, suffers from many drawbacks making the…
Improving Randomized Learning of Feedforward Neural Networks by Appropriate Generation of Random Parameters
Grzegorz Dudek
In this work, a method of random parameters generation for randomized learning of a single-hidden-layer feedforward neural network is proposed. The method firstly, randomly selects…