99 citations · 312 across the 30 of their papers we have counts for
9 papers · 1 filter
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs
Sevvandi Kandanaarachchi, Conrad Sanderson, Rob J. Hyndman
Detecting anomalies in a temporal sequence of graphs can be applied is areas such as the detection of accidents in transport networks and cyber attacks in computer networks. Existi…
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…
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…
Monash Time Series Forecasting Archive
Rakshitha Godahewa, Christoph Bergmeir, Geoffrey I. Webb +2
Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area. Global forecasting models that are t…
Computationally Efficient Learning of Statistical Manifolds
Fan Cheng, Anastasios Panagiotelis, Rob J Hyndman
Analyzing high-dimensional data with manifold learning algorithms often requires searching for the nearest neighbors of all observations. This presents a computational bottleneck i…
Model selection in reconciling hierarchical time series
Mahdi Abolghasemi, Rob J Hyndman, Evangelos Spiliotis +1
Model selection has been proven an effective strategy for improving accuracy in time series forecasting applications. However, when dealing with hierarchical time series, apart fro…
Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality
Pablo Montero-Manso, Rob J Hyndman
Forecasting groups of time series is of increasing practical importance, e.g. forecasting the demand for multiple products offered by a retailer or server loads within a data cente…
Hierarchical forecast reconciliation with machine learning
Evangelos Spiliotis, Mahdi Abolghasemi, Rob J Hyndman +2
Hierarchical forecasting methods have been widely used to support aligned decision-making by providing coherent forecasts at different aggregation levels. Traditional hierarchical…
Machine learning applications in time series hierarchical forecasting
Mahdi Abolghasemi, Rob J Hyndman, Garth Tarr +1
Hierarchical forecasting (HF) is needed in many situations in the supply chain (SC) because managers often need different levels of forecasts at different levels of SC to make a de…