Christoph Bergmeir
Cited by
Cited by
On the use of cross-validation for time series predictor evaluation
C Bergmeir, JM Benítez
Information Sciences 191, 192-213, 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
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
Neural networks in R using the Stuttgart neural network simulator: RSNNS
CN Bergmeir, JM Benítez Sánchez
American Statistical Association, 2012
Recurrent neural networks for time series forecasting: Current status and future directions
H Hewamalage, C Bergmeir, K Bandara
International Journal of Forecasting 37 (1), 388-427, 2021
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
frbs: Fuzzy rule-based systems for classification and regression in R
LS Riza, C Bergmeir, F Herrera, JM Benítez
Journal of statistical software 65, 1-30, 2015
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach
K Bandara, C Bergmeir, S Smyl
Expert systems with applications 140, 112896, 2020
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
Forecasting functions for time series and linear models
R Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, ...
R package version 6, 2015
Package ‘forecast’
RJ Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, ...
Online] https://cran. r-project. org/web/packages/forecast/forecast. pdf, 2020
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
Forecasting: theory and practice
F Petropoulos, D Apiletti, V Assimakopoulos, MZ Babai, DK Barrow, ...
International Journal of Forecasting, 2022
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
Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study
S Nanayakkara, S Fogarty, M Tremeer, K Ross, B Richards, C Bergmeir, ...
PLoS medicine 15 (11), e1002709, 2018
On the usefulness of cross-validation for directional forecast evaluation
C Bergmeir, M Costantini, JM Benítez
Computational Statistics & Data Analysis 76, 132-143, 2014
LSTM-MSNet: Leveraging forecasts on sets of related time series with multiple seasonal patterns
K Bandara, C Bergmeir, H Hewamalage
IEEE transactions on neural networks and learning systems 32 (4), 1586-1599, 2020
Comparing calibration approaches for 3D ultrasound probes
C Bergmeir, M Seitel, C Frank, RD Simone, HP Meinzer, I Wolf
International journal of computer assisted radiology and surgery 4 (2), 203-213, 2009
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 8, 805-815, 2017
The system can't perform the operation now. Try again later.
Articles 1–20