Forecasting oil price trends using wavelets and hidden Markov models
By Edmundo de Souza e Silva, Luiz Fernando Loureiro Legey. 2010
90 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Economics and Econometrics, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/317718725e53ceb61f6c16f4c98262ebad4c009d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Analytical Chemistry
- Lean Yu
- Kaijian He
- Kin Keung Lai
- Christoph Bergmeir
- Feng Li
- John E. Boylan
- Fotios Petropoulos
- Ricardo J. Bessa
In Economics and Econometrics
- Fotios Petropoulos
- Konstantinos Nikolopoulos
- Rob J Hyndman
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Evangelos Spiliotis
- Spyros Makridakis
- Christoph Bergmeir
In Management Science and Operations Research
- Fotios Petropoulos
- Rob J Hyndman
- Konstantinos Nikolopoulos
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Spyros Makridakis
- Evangelos Spiliotis
- Christoph Bergmeir
Citations in the corpus
- Forecasting: theory and practice (2020, methodology): “forecasting has focused predominantly on the econometric models, such as VAR, ARCH-type, ARIMA, and Markov models (see, for example, Mirmirani and Li, 2004; Agnolucci, 2009; Mohammadi and Su, 2010; e Silva et al., 2010). In the forecasting literature, unit root tests are commonly applied to examine ”
- Crude oil price analysis and forecasting using wavelet decomposed ensemble model (2012, methodology): “[42] uses wavelet analysis to remove high frequency noises, then uses Hidden Markov Model (HMM) to predict future price movement [42].”