Consistent cross-validatory model-selection for dependent data: hv-block cross-validation
By Jeffrey S. Racine. 2000
228 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/fec8709c61374672305f5f07d1af884254a01c24
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Fotios Petropoulos
- Konstantinos Nikolopoulos
- Rob J Hyndman
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Evangelos Spiliotis
- Spyros Makridakis
- Christoph Bergmeir
In Finance
- David F. Hendry
- David E. Rapach
- Michael P. Clements
- Jennifer L. Castle
- Feng Li
- Xiaoqian Wang
- Yanfei Kang
- Allan G. Timmermann
In Statistics and Probability
- Konstantinos Nikolopoulos
- Vassilios Assimakopoulos
- Nikolaos Kourentzes
- Fotios Petropoulos
- Evangelos Spiliotis
- Spyros Makridakis
- Christoph Bergmeir
- Stephan Kolassa
Citations in the corpus
- A note on the validity of cross-validation for evaluating autoregressive time series prediction (2018, unknown): “As a result, several CV techniques have been developed especially for the dependent case (Györfi et al., 1989; Burman and Nolan, 1992; Burman et al., 1994; McQuarrie and Tsai, 1998; Racine, 2000; Kunst, 2008).”
- Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques (2016, methodology): “We also used the hv-Cross Validation procedure of Racine (2000), but this did not improve the results and was omitted.”
- Predicting individual stem volumes of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR (2005, methodology): “For cross validation, we applied the leave-one-out method (Racine 2000).”
- Introduction to machine learning for brain imaging (2011, methodology): “(cid:5) Following the notation in Racine (2000), we denote the h -block by x (cid:6) .”
- Evaluating time series forecasting models: an empirical study on performance estimation methods (2019, methodology): “The hv-Blocked Cross-Validation (CV-hvBl) proposed by Racine [34] extends blocked cross-validation to further increase the independence among observations.”
- Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure (2017, unknown): “When models are intended to predict in time, blocks may be drawn in the same manner (i.e. blocks of contiguous time) to better ensure independence between cross-validation folds (Burman et al. 1994, Racine 2000, Bergmeir and Benitez 2012).”
- Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices (2018, methodology): “This estimation method is often used in time series analysis, see. e.g. Racine (2000).”
- Forecasting: theory and practice (2020, methodology): “…CV procedure, to overcome problems with serial correlations, blocked CV approaches have been proposed in the literature, where the folds are chosen in blocks (Racine, 2000; Bergmeir and Benı́tez, 2012) and/or data around the points used for testing are omitted (Racine, 2000; Burman et al., 1994).”