Meta-learning for time series forecasting and forecast combination

By Christiane Lemke, Bogdan Gabrys. 2010

261 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Fotios Petropoulos
2. Rob J Hyndman
3. Konstantinos Nikolopoulos
4. Nikolaos Kourentzes
5. Vassilios Assimakopoulos
6. Spyros Makridakis
7. Sven F. Crone
8. Christoph Bergmeir

In Statistics and Probability

1. Konstantinos Nikolopoulos
2. Vassilios Assimakopoulos
3. Nikolaos Kourentzes
4. Fotios Petropoulos
5. Evangelos Spiliotis
6. Spyros Makridakis
7. Sven F. Crone
8. Christoph Bergmeir

Citations in the corpus

• Metalearning: a survey of trends and technologies (2013, background): “The bias in base-learning according to this theory is fixed, whereas metalearning tries to choose the right bias dynamically.”
• Meta-learning with neural networks and landmarking for forecasting model selection an empirical evaluation of different feature sets applied to industry data (2016, methodology): “While past studies about meta-learning for selecting time series forecasting models mostly used simple statistical or information-theoretic meta-features as well as model-based meta-features [16], [17], [18], [19], [27], landmarkers have been neglected so far.”
• Towards Automatic Composition of Multicomponent Predictive Systems (2016, background): “active testing [7], meta-learning [8] and information theory [9].”
• Forecasting: theory and practice (2020, background): “, 2018a), the creation of diverse forecasts (Brown et al., 2005a; Lemke and Gabrys, 2010), and the pooling of forecasts”