32 citations · 45 across the 10 of their papers we have counts for
10 papers
Adaptive Dependency Learning Graph Neural Networks
Abishek Sriramulu, Nicolas Fourrier, Christoph Bergmeir
Graph Neural Networks (GNN) have recently gained popularity in the forecasting domain due to their ability to model complex spatial and temporal patterns in tasks such as traffic f…
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…
Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approach
Xueying Long, Quang Bui, Grady Oktavian +6
The recent M5 competition has advanced the state-of-the-art in retail forecasting. However, we notice important differences between the competition challenge and the challenges we…
On Forecast Stability
Rakshitha Godahewa, Christoph Bergmeir, Zeynep Erkin Baz +4
Forecasts are typically not produced in a vacuum but in a business context, where forecasts are generated on a regular basis and interact with each other. For decisions, it may be…
Handling Concept Drift in Global Time Series Forecasting
Ziyi Liu, Rakshitha Godahewa, Kasun Bandara +1
Machine learning (ML) based time series forecasting models often require and assume certain degrees of stationarity in the data when producing forecasts. However, in many real-worl…
Predict+Optimize Problem in Renewable Energy Scheduling
Christoph Bergmeir, Frits de Nijs, Evgenii Genov +25
Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are criti…
SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series Forecasting
Rakshitha Godahewa, Geoffrey I. Webb, Daniel Schmidt +1
Threshold Autoregressive (TAR) models have been widely used by statisticians for non-linear time series forecasting during the past few decades, due to their simplicity and mathema…
FRANS: Automatic Feature Extraction for Time Series Forecasting
Alexey Chernikov, Chang Wei Tan, Pablo Montero-Manso +1
Feature extraction methods help in dimensionality reduction and capture relevant information. In time series forecasting (TSF), features can be used as auxiliary information to ach…
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…
LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts
Dilini Rajapaksha, Christoph Bergmeir, Rob J Hyndman
Global Forecasting Models (GFM) that are trained across a set of multiple time series have shown superior results in many forecasting competitions and real-world applications compa…