10 citations · 28 across the 18 of their papers we have counts for
18 papers · 1 filter
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…
Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
Slawek Smyl, Grzegorz Dudek, Paweł Pełka
In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) an…
Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study
Grzegorz Dudek, Slawek Smyl, Paweł Pełka
This paper compares recurrent neural networks (RNNs) with different types of gated cells for forecasting time series with multiple seasonality. The cells we compare include classic…
Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting
Grzegorz Dudek
Time series forecasting is a challenging problem particularly when a time series expresses multiple seasonality, nonlinear trend and varying variance. In this work, to forecast com…
ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
Slawek Smyl, Grzegorz Dudek, Paweł Pełka
Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance. This paper proposes a…
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting
Slawek Smyl, Grzegorz Dudek, Paweł Pełka
Short-term load forecasting (STLF) is challenging due to complex time series (TS) which express three seasonal patterns and a nonlinear trend. This paper proposes a novel hybrid hi…
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…
Data-Driven Learning of Feedforward Neural Networks with Different Activation Functions
Grzegorz Dudek
This work contributes to the development of a new data-driven method (D-DM) of feedforward neural networks (FNNs) learning. This method was proposed recently as a way of improving…
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…
Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality
Grzegorz Dudek
This work contributes to the development of neural forecasting models with novel randomization-based learning methods. These methods improve the fitting abilities of the neural mod…
N-BEATS neural network for mid-term electricity load forecasting
Boris N. Oreshkin, Grzegorz Dudek, Paweł Pełka +1
This paper addresses the mid-term electricity load forecasting problem. Solving this problem is necessary for power system operation and planning as well as for negotiating forward…
Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods
Paweł Pełka, Grzegorz Dudek
This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs). PSFMs applied in this study include -nearest ne…
Are Direct Links Necessary in RVFL NNs for Regression?
Grzegorz Dudek
A random vector functional link network (RVFL) is widely used as a universal approximator for classification and regression problems. The big advantage of RVFL is fast training wit…
Pattern Similarity-based Machine Learning Methods for Mid-term Load Forecasting: A Comparative Study
Grzegorz Dudek, Paweł Pełka
Pattern similarity-based methods are widely used in classification and regression problems. Repeated, similar-shaped cycles observed in seasonal time series encourage to apply thes…
A Constructive Approach for Data-Driven Randomized Learning of Feedforward Neural Networks
Grzegorz Dudek
Feedforward neural networks with random hidden nodes suffer from a problem with the generation of random weights and biases as these are difficult to set optimally to obtain a good…
Generating Random Parameters in Feedforward Neural Networks with Random Hidden Nodes: Drawbacks of the Standard Method and How to Improve It
Grzegorz Dudek
The standard method of generating random weights and biases in feedforward neural networks with random hidden nodes, selects them both from the uniform distribution over the same f…
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…
Data-Driven Randomized Learning of Feedforward Neural Networks
Grzegorz Dudek
Randomized methods of neural network learning suffer from a problem with the generation of random parameters as they are difficult to set optimally to obtain a good projection spac…