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Christoph Bergmeir
Christoph Bergmeir
University of Granada
Verified email at monash.edu - Homepage
Title
Cited by
Cited by
Year
On the use of cross-validation for time series predictor evaluation
C Bergmeir, JM Benítez
Information Sciences 191, 192-213, 2012
9042012
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
6752021
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
6022018
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
4382013
Forecasting: theory and practice
F Petropoulos, D Apiletti, V Assimakopoulos, MZ Babai, DK Barrow, ...
International Journal of Forecasting 38 (3), 705-871, 2022
3862022
Neural networks in R using the Stuttgart neural network simulator: RSNNS
CN Bergmeir, JM Benítez Sánchez
American Statistical Association, 2012
3712012
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
3242016
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
2952020
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
2172015
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
1922014
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
Neural Information Processing: 26th International Conference, ICONIP 2019 …, 2019
1602019
Package ‘forecast’
RJ Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, ...
Online] https://cran. r-project. org/web/packages/forecast/forecast. pdf, 2020
1572020
Forecasting functions for time series and linear models
R Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, ...
R package version 6, 2015
1502015
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
1432012
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
1422018
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
1302020
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
1072018
Improving the accuracy of global forecasting models using time series data augmentation
K Bandara, H Hewamalage, YH Liu, Y Kang, C Bergmeir
Pattern Recognition 120, 108148, 2021
902021
On the usefulness of cross-validation for directional forecast evaluation
C Bergmeir, M Costantini, JM Benítez
Computational Statistics & Data Analysis 76, 132-143, 2014
902014
Neuralprophet: Explainable forecasting at scale
O Triebe, H Hewamalage, P Pilyugina, N Laptev, C Bergmeir, ...
arXiv preprint arXiv:2111.15397, 2021
812021
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Articles 1–20