1 citations · 1 across the 3 of their papers we have counts for
6 papers
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…
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…
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…