Search Results for author: Paweł Pełka

Found 7 papers, 3 papers with code

Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study

no code implementations17 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.

Load Forecasting Time Series

ES-dRNN with Dynamic Attention for Short-Term Load Forecasting

1 code implementation2 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.

Load Forecasting Time Series

ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting

1 code implementation5 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.

Load Forecasting Time Series

Ensembles of Randomized NNs for Pattern-based Time Series Forecasting

no code implementations8 Jul 2021 Grzegorz Dudek, Paweł Pełka

In this work, we propose an ensemble forecasting approach based on randomized neural networks.

Time Series Time Series Forecasting

N-BEATS neural network for mid-term electricity load forecasting

1 code implementation24 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.

Decision Making Load Forecasting +1

Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods

no code implementations29 Mar 2020 Paweł Pełka, Grzegorz Dudek

This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs).

Time Series

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