1.4k citations · 1.6k across the 8 of their papers we have counts for
9 papers
The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond
Direnc Pekaslan, Jose Maria Alonso-Moral, Kasun Bandara +17
This paper presents the real-world smart-meter dataset and offers an analysis of solutions derived from the Energy Prediction Technical Challenges, focusing primarily on two key co…
Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices
Hansika Hewamalage, Klaus Ackermann, Christoph Bergmeir
Machine Learning (ML) and Deep Learning (DL) methods are increasingly replacing traditional methods in many domains involved with important decision making activities. DL technique…
NeuralProphet: Explainable Forecasting at Scale
Oskar Triebe, Hansika Hewamalage, Polina Pilyugina +3
We introduce NeuralProphet, a successor to Facebook Prophet, which set an industry standard for explainable, scalable, and user-friendly forecasting frameworks. With the proliferat…
A Look at the Evaluation Setup of the M5 Forecasting Competition
Hansika Hewamalage, Pablo Montero-Manso, Christoph Bergmeir +1
Forecast evaluation plays a key role in how empirical evidence shapes the development of the discipline. Domain experts are interested in error measures relevant for their decision…
Global Models for Time Series Forecasting: A Simulation Study
Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara
In the current context of Big Data, the nature of many forecasting problems has changed from predicting isolated time series to predicting many time series from similar sources. Th…
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation
Kasun Bandara, Hansika Hewamalage, Yuan-Hao Liu +2
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and…
LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns
Kasun Bandara, Christoph Bergmeir, Hansika Hewamalage
Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays. Accounting for the multi-seasonal patterns becomes necessa…
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
Hansika Hewamalage, Christoph Bergmeir, Kasun Bandara
Recurrent Neural Networks (RNN) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statisti…
Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology
Kasun Bandara, Peibei Shi, Christoph Bergmeir +3
Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce fore…