Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles
By James W. Taylor. 2010
100 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/6017217de7bc5a7094747417c2b9ee74176aa003
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- Rob J Hyndman
- Ralph D. Snyder
- Alysha M. De Livera
- Fotios Petropoulos
- Pierre Pinson
- Rafal Weron
- Spyros Makridakis
- Nikolaos Kourentzes
In Management Science and Operations Research
- Rob J Hyndman
- Fotios Petropoulos
- Konstantinos Nikolopoulos
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Spyros Makridakis
- Evangelos Spiliotis
- Christoph Bergmeir
In Statistics and Probability
- Fotios Petropoulos
- Konstantinos Nikolopoulos
- Rob J Hyndman
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Spyros Makridakis
- Evangelos Spiliotis
- James W. Taylor
Citations in the corpus
- Short-Term Load Forecasting With Exponentially Weighted Methods (2012, methodology): “In [6], it is explained that separate smoothing of the level is”
- Forecasting: theory and practice (2020, methodology): “Since then several advanced time series methods have been developed and evaluated (Taylor, 2010; De Livera et al., 2011; Taylor and Snyder, 2012), including artificial neural networks (Millán-Ruiz and Hidalgo, 2013; Pacheco et al., 2009; Li et al., 2011) and models for density forecasting (Taylor,…”
- Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing (2011, methodology): “To avoid the potentially large optimization problem, the initial states are usually approximated with various heuristics (Taylor 2003, 2010b; Gould et al. 2008), a practice that does not lead to optimized seed states.”