18 citations · 18 across the 5 of their papers we have counts for
5 papers
Scalable Transformer for High Dimensional Multivariate Time Series Forecasting
Xin Zhou, Weiqing Wang, Wray Buntine +4
Deep models for Multivariate Time Series (MTS) forecasting have recently demonstrated significant success. Channel-dependent models capture complex dependencies that channel-indepe…
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…
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…
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…