99 citations · 312 across the 30 of their papers we have counts for
7 papers · 1 filter
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…
MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns
Kasun Bandara, Rob J Hyndman, Christoph Bergmeir
The decomposition of time series into components is an important task that helps to understand time series and can enable better forecasting. Nowadays, with high sampling rates lea…
Detection of cybersecurity attacks through analysis of web browsing activities using principal component analysis
Insha Ullah, Kerrie Mengersen, Rob J Hyndman +1
Organizations such as government departments and financial institutions provide online service facilities accessible via an increasing number of internet connected devices which ma…
Understanding links between water-quality variables and nitrate concentration in freshwater streams using high-frequency sensor data
Claire Kermorvant, Benoit Liquet, Guy Litt +5
Real time monitoring using in situ sensors is becoming a common approach for measuring water quality within watersheds. High frequency measurements produce big data sets that prese…
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…