56 citations · 73 across the 14 of their papers we have counts for
15 papers
Probabilistic Multi-Regional Solar Power Forecasting with Any-Quantile Recurrent Neural Networks
Slawek Smyl, Paweł Pełka, Grzegorz Dudek
The increasing penetration of photovoltaic (PV) generation introduces significant uncertainty into power system operation, necessitating forecasting approaches that extend beyond d…
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…
Predicting Customer Lifetime Value Using Recurrent Neural Net
Huigang Chen, Edwin Ng, Slawek Smyl +1
This paper introduces a recurrent neural network approach for predicting user lifetime value in Software as a Service (SaaS) applications. The approach accounts for three connected…
Fast Gibbs sampling for the local and global trend Bayesian exponential smoothing model
Xueying Long, Daniel F. Schmidt, Christoph Bergmeir +1
In Smyl et al. [Local and global trend Bayesian exponential smoothing models. International Journal of Forecasting, 2024.], a generalised exponential smoothing model was proposed t…
Context Neural Networks: A Scalable Multivariate Model for Time Series Forecasting
Abishek Sriramulu, Christoph Bergmeir, Slawek Smyl
Real-world time series often exhibit complex interdependencies that cannot be captured in isolation. Global models that model past data from multiple related time series globally w…
Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand
Slawek Smyl, Boris N. Oreshkin, Paweł Pełka +1
Power systems operate under uncertainty originating from multiple factors that are impossible to account for deterministically. Distributional forecasting is used to control and mi…
Local and Global Trend Bayesian Exponential Smoothing Models
Slawek Smyl, Christoph Bergmeir, Alexander Dokumentov +3
This paper describes a family of seasonal and non-seasonal time series models that can be viewed as generalisations of additive and multiplicative exponential smoothing models, to…
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…
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 Localised Models for Time Series Forecasting
Rakshitha Godahewa, Kasun Bandara, Geoffrey I. Webb +2
With large quantities of data typically available nowadays, forecasting models that are trained across sets of time series, known as Global Forecasting Models (GFM), are regularly…
Orbit: Probabilistic Forecast with Exponential Smoothing
Edwin Ng, Zhishi Wang, Huigang Chen +2
Time series forecasting is an active research topic in academia as well as industry. Although we see an increasing amount of adoptions of machine learning methods in solving some o…
A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting
Grzegorz Dudek, Paweł Pełka, Slawek Smyl
This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting. The model combines exponential smoothing (ETS), advanced Long Short-Term Memory (LST…
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering Approach
Kasun Bandara, Christoph Bergmeir, Slawek Smyl
With the advent of Big Data, nowadays in many applications databases containing large quantities of similar time series are available. Forecasting time series in these domains with…