| 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 | 1737 | 2021 |
| On the use of cross-validation for time series predictor evaluation C Bergmeir, JM Benítez Information Sciences 191, 192-213, 2012 | 1458 | 2012 |
| Forecasting: theory and practice F Petropoulos, D Apiletti, V Assimakopoulos, MZ Babai, DK Barrow, ... International Journal of forecasting 38 (3), 705-871, 2022 | 1274 | 2022 |
| 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 | 885 | 2018 |
| 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 | 590 | 2020 |
| 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 | 534 | 2013 |
| 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 | 479 | 2016 |
| Neural networks in R using the Stuttgart neural network simulator: RSNNS C Bergmeir, JM Benítez Journal of Statistical Software 46, 1-26, 2012 | 436 | 2012 |
| Monash time series forecasting archive R Godahewa, C Bergmeir, GI Webb, RJ Hyndman, P Montero-Manso arXiv preprint arXiv:2105.06643, 2021 | 328 | 2021 |
| 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 | 285 | 2019 |
| 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 | 254 | 2015 |
| MultiRocket: multiple pooling operators and transformations for fast and effective time series classification CW Tan, A Dempster, C Bergmeir, GI Webb arXiv preprint arXiv:2102.00457, 2021 | 252 | 2021 |
| 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 | 247 | 2020 |
| Forecast evaluation for data scientists: common pitfalls and best practices H Hewamalage, K Ackermann, C Bergmeir Data Mining and Knowledge Discovery 37 (2), 788-832, 2023 | 238 | 2023 |
| Neuralprophet: Explainable forecasting at scale O Triebe, H Hewamalage, P Pilyugina, N Laptev, C Bergmeir, ... arXiv preprint arXiv:2111.15397, 2021 | 229 | 2021 |
| 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 | 222 | 2014 |
| Package ‘forecast’ RJ Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, ... Online] https://cran. r-project. org/web/packages/forecast/forecast. pdf, 2020 | 218 | 2020 |
| 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 | 211 | 2018 |
| MSTL: A seasonal-trend decomposition algorithm for time series with multiple seasonal patterns K Bandara, RJ Hyndman, C Bergmeir International Journal of Operational Research 52 (1), 79-98, 2025 | 201 | 2025 |
| 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 | 199 | 2021 |
Jose M. BenitezFull Professor, Universidad de GranadaVerified email at decsai.ugr.es
