Christoph Bergmeir
TitleCited byYear
forecast: Forecasting functions for time series and linear models
RJ Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, ...
Actigraph GT3X: validation and determination of physical activity intensity cut points
A Santos-Lozano, F Santin-Medeiros, G Cardon, G Torres-Luque, ...
Int J Sports Med 10, 0033-1337945, 2013
On the use of cross-validation for time series predictor evaluation
C Bergmeir, JM Benítez
Information Sciences 191, 192-213, 2012
Neural networks in R using the Stuttgart neural network simulator: RSNNS
CN Bergmeir, JM Benítez Sánchez
American Statistical Association, 2012
A note on the validity of cross-validation for evaluating autoregressive time series prediction
C Bergmeir, RJ Hyndman, B Koo
Computational Statistics & Data Analysis 120, 70-83, 2018
frbs: Fuzzy rule-based systems for classification and regression in R
LS Riza, CN Bergmeir, F Herrera Triguero, JM Benítez Sánchez
American Statistical Association, 2015
Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “RoughSets”
LS Riza, A Janusz, C Bergmeir, C Cornelis, F Herrera, D Śle, JM Benítez
Information Sciences 287, 68-89, 2014
Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework
C Bergmeir, MG Silvente, JM Benítez
Computer methods and programs in biomedicine 107 (3), 497-512, 2012
Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation
C Bergmeir, RJ Hyndman, JM Benítez
International journal of forecasting 32 (2), 303-312, 2016
Comparing calibration approaches for 3D ultrasound probes
C Bergmeir, M Seitel, C Frank, R De Simone, HP Meinzer, I Wolf
International journal of computer assisted radiology and surgery 4 (2), 203, 2009
On the usefulness of cross-validation for directional forecast evaluation
C Bergmeir, M Costantini, JM Benítez
Computational Statistics & Data Analysis 76, 132-143, 2014
Exploring the sources of uncertainty: Why does bagging for time series forecasting work?
F Petropoulos, RJ Hyndman, C Bergmeir
European Journal of Operational Research 268 (2), 545-554, 2018
On the stopping criteria for k-Nearest Neighbor in positive unlabeled time series classification problems
M González, C Bergmeir, I Triguero, Y Rodríguez, JM Benítez
Information Sciences 328, 42-59, 2016
forecast: Forecasting functions for time series and linear models, 2013, r package version 4.06
RJ Hyndman, G Athanasopoulos, S Razbash, D Schmidt, Z Zhou, Y Khan, ...
URL http://github. com/robjhyndman/forecast, 2017
Forecasting across time series databases using long short-term memory networks on groups of similar series
K Bandara, C Bergmeir, S Smyl
arXiv preprint arXiv:1710.03222, 2017
Segmentation of cervical cell images using mean-shift filtering and morphological operators
C Bergmeir, MG Silvente, JE López-Cuervo, JM Benítez
Medical Imaging 2010: Image Processing 7623, 76234C, 2010
Learning from data using the R package" FRBS"
LS Riza, C Bergmeir, F Herrera, JM Benitez
2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2149-2155, 2014
Mcomp: Data from the M-competitions
RJ Hyndman, M Akram, C Bergmeir
URL http://robjhyndman. com/software/mcomp, 2013
Time series modeling and forecasting using memetic algorithms for regime-switching models
C Bergmeir, I Triguero, D Molina, JL Aznarte, JM Benítez
IEEE transactions on neural networks and learning systems 23 (11), 1841-1847, 2012
Sales demand forecast in e-commerce using a long short-term memory neural network methodology
K Bandara, P Shi, C Bergmeir, H Hewamalage, Q Tran, B Seaman
International Conference on Neural Information Processing, 462-474, 2019
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