no code implementations • 26 Apr 2024 • Slawek Smyl, Boris N. Oreshkin, Paweł Pełka, Grzegorz Dudek
We show that our general approach can be seamlessly applied to two distinct neural architectures leading to the state-of-the-art distributional forecasting results in the context of short-term electricity demand forecasting task.
1 code implementation • 18 Dec 2022 • Slawek Smyl, Grzegorz Dudek, Paweł Pełka
These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information.
1 code implementation • 21 Apr 2022 • Grzegorz Dudek
To deal with heteroscedasticity in time series, the method proposed in this work -- a seasonal-trend-dispersion decomposition (STD) -- extracts the trend, seasonal component and component related to the dispersion of the time series.
no code implementations • 17 Mar 2022 • 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.
no code implementations • 2 Mar 2022 • Grzegorz Dudek
The latter two methods are employed to ensure similar forecasting tasks are solved by all ensemble members, which justifies the use of exactly the same base models at all stages of ensembling.
1 code implementation • 2 Mar 2022 • 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.
1 code implementation • 5 Dec 2021 • Slawek Smyl, Grzegorz Dudek, Paweł Pełka
A multi-layer RNN is equipped with a new type of dilated recurrent cell designed to efficiently model both short and long-term dependencies in TS.
no code implementations • 8 Jul 2021 • Grzegorz Dudek, Paweł Pełka
In this work, we propose an ensemble forecasting approach based on randomized neural networks.
no code implementations • 4 Jul 2021 • Grzegorz Dudek
However, the problem in randomized learning is how to determine the random parameters.
no code implementations • 4 Jul 2021 • Grzegorz Dudek
This work contributes to the development of neural forecasting models with novel randomization-based learning methods.
no code implementations • 4 Jul 2021 • Grzegorz Dudek
This work contributes to the development of a new data-driven method (D-DM) of feedforward neural networks (FNNs) learning.
1 code implementation • 24 Sep 2020 • Boris N. Oreshkin, Grzegorz Dudek, Paweł Pełka, Ekaterina Turkina
We show that our proposed deep neural network modeling approach based on the deep neural architecture is effective at solving the mid-term electricity load forecasting problem.
no code implementations • 22 Apr 2020 • Paweł{ }Pełka, Grzegorz Dudek
The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition.
no code implementations • 29 Mar 2020 • Grzegorz Dudek, Paweł Pełka, Slawek Smyl
This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting.
no code implementations • 29 Mar 2020 • Grzegorz Dudek
A random vector functional link network (RVFL) is widely used as a universal approximator for classification and regression problems.
no code implementations • 29 Mar 2020 • Paweł Pełka, Grzegorz Dudek
This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs).
no code implementations • 3 Mar 2020 • Grzegorz Dudek, Pawel Pelka
In the experimental part of the work the proposed models were used to forecasting the monthly demand for 35 European countries.
no code implementations • 4 Sep 2019 • 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 projection space.
no code implementations • 16 Aug 2019 • 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 fixed interval.
no code implementations • 15 Aug 2019 • Grzegorz Dudek
In this work, a method of random parameters generation for randomized learning of a single-hidden-layer feedforward neural network is proposed.
no code implementations • 11 Aug 2019 • Grzegorz Dudek
In this work, a method which adjusts the random parameters, representing the slopes and positions of the sigmoids, to the target function features is proposed.
no code implementations • 13 Oct 2017 • Grzegorz Dudek
In these networks the weights and biases of hidden nodes determining the nonlinear feature mapping are set randomly and are not learned.