4 citations · 8 across the 10 of their papers we have counts for
19 papers
STD: A Seasonal-Trend-Dispersion Decomposition of Time Series
Grzegorz Dudek
The decomposition of a time series is an essential task that helps to understand its very nature. It facilitates the analysis and forecasting of complex time series expressing vari…
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…
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…