Christoph Bergmeir
Category
PhD - Maria ZambranoContact
bergmeir@uwxsgI1gr.es
Organization
UGR
| Total | From 2019: | |
|---|---|---|
| Citas | Total: 9165 | From 2019: 7510 |
| Índice H | Total: 34 | From 2019: 32 |
| Índice i10 | Total: 70 | From 2019: 63 |
Papers (128)
| Title | Authors | Year |
|---|---|---|
| How Well Can Social Scientists Forecast Societal Change? | I Grossmann, C Bergmeir, P Slattery. | 2024 |
| Forecast evaluation for data scientists: common pitfalls and best practices | H Hewamalage, K Ackermann, C Bergmeir. | 2023 |
| Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices | M Mohaimenuzzaman, C Bergmeir, I West, B Meyer. | 2023 |
| LoMEF: A framework to produce local explanations for global model time series forecasts | D Rajapaksha, C Bergmeir, RJ Hyndman. | 2023 |
| Adaptive dependency learning graph neural networks | A Sriramulu, N Fourrier, C Bergmeir. | 2023 |
| Insights into the accuracy of social scientists’ forecasts of societal change | Nature human behaviour 7 (4), 484-501, 2023 . | 2023 |
| An accurate and fully-automated ensemble model for weekly time series forecasting | R Godahewa, C Bergmeir, GI Webb, P Montero-Manso. | 2023 |
| An overview of clustering methods with guidelines for application in mental health research | CX Gao, D Dwyer, Y Zhu, CL Smith, L Du, KM Filia, J Bayer, JM Menssink, .... | 2023 |
| Handling Concept Drift in Global Time Series Forecasting | Z Liu, R Godahewa, K Bandara, C Bergmeir. | 2023 |
| SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting | R Godahewa, GI Webb, D Schmidt, C Bergmeir. | 2023 |
| Common Pitfalls and Better Practices in Forecast Evaluation for Data Scientists | C Bergmeir. | 2023 |
| Common Pitfalls and Better Practices in Forecast Evaluation for Data Scientists. | C Bergmeir. | 2023 |
| Tree-based survival analysis improves mortality prediction in cardiac surgery | JC Penny-Dimri, C Bergmeir, CM Reid, J Williams-Spence, LA Perry, .... | 2023 |
| Paying attention to cardiac surgical risk: An interpretable machine learning approach using an uncertainty-aware attentive neural network | JC Penny-Dimri, C Bergmeir, CM Reid, J Williams-Spence, AD Cochrane, .... | 2023 |
| Deep Active Audio Feature Learning in Resource-Constrained Environments | M Mohaimenuzzaman, C Bergmeir, B Meyer. | 2023 |
| Local and Global Trend Bayesian Exponential Smoothing Models | S Smyl, C Bergmeir, A Dokumentov, E Wibowo, D Schmidt. | 2023 |
| On Forecast Stability | R Godahewa, C Bergmeir, ZE Baz, C Zhu, Z Song, S García, D Benavides. | 2023 |
| Time series adversarial attacks: an investigation of smooth perturbations and defense approaches | G Pialla, H Ismail Fawaz, M Devanne, J Weber, L Idoumghar, PA Muller, .... | 2023 |
| The Energy Prediction Smart-Meter Dataset: Analysis of Previous Competitions and Beyond | D Pekaslan, JM Alonso-Moral, K Bandara, C Bergmeir, .... | 2023 |
| Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approach | X Long, Q Bui, G Oktavian, DF Schmidt, C Bergmeir, R Godahewa, .... | 2023 |
| Comparison and Evaluation of Methods for a Predict+ Optimize Problem in Renewable Energy | C Bergmeir, F de Nijs, A Sriramulu, M Abolghasemi, R Bean, J Betts, .... | 2022 |
| Forecasting: theory and practice | F Petropoulos, D Apiletti, V Assimakopoulos, MZ Babai, DK Barrow, .... | 2022 |
| MultiRocket: multiple pooling operators and transformations for fast and effective time series classification | CW Tan, A Dempster, C Bergmeir, GI Webb. | 2022 |
| Global models for time series forecasting: A simulation study | H Hewamalage, C Bergmeir, K Bandara. | 2022 |
| Model selection in reconciling hierarchical time series | M Abolghasemi, RJ Hyndman, E Spiliotis, C Bergmeir. | 2022 |
| Pruning vs XNOR-net: A comprehensive study of deep learning for audio classification on edge-devices | M Mohaimenuzzaman, C Bergmeir, B Meyer. | 2022 |
| LImref: Local interpretable model agnostic rule-based explanations for forecasting, with an application to electricity smart meter data | D Rajapaksha, C Bergmeir. | 2022 |
| Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions | M Abolghasemi, G Tarr, C Bergmeir. | 2022 |
| Smooth perturbations for time series adversarial attacks | G Pialla, HI Fawaz, M Devanne, J Weber, L Idoumghar, PA Muller, .... | 2022 |
| A Generative Deep Learning Framework Across Time Series to Optimize the Energy Consumption of Air Conditioning Systems | R Godahewa, C Deng, A Prouzeau, C Bergmeir. | 2022 |
| Machine learning to predict adverse outcomes after cardiac surgery: A systematic review and meta‐analysis | JC Penny‐Dimri, C Bergmeir, L Perry, L Hayes, R Bellomo, JA Smith. | 2022 |
| Probabilistic causal effect estimation with global neural network forecasting models | P Grecov, AN Prasanna, K Ackermann, S Campbell, D Scott, DI Lubman, .... | 2022 |
| Dealing with missing data using attention and latent space regularization | JC Penny-Dimri, C Bergmeir, J Smith. | 2022 |
| Causal Effect Estimation with Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand | A Nandipura Prasanna, P Grecov, A Dieyu Weng, C Bergmeir. | 2022 |
| FRANS: Automatic Feature Extraction for Time Series Forecasting | A Chernikov, CW Tan, P Montero-Manso, C Bergmeir. | 2022 |
| RNN-BOF: A Multivariate Global Recurrent Neural Network for Binary Outcome Forecasting of Inpatient Aggression | A Quinn, M Simmons, B Spivak, C Bergmeir. | 2022 |
| Evaluating individual heterogeneity in mental health research: an overview of clustering methods and guidelines for applications | CX Gao, D Dwyer, Y Zhu, CL Smith, L Du, KM Filia, JMM Bayer, .... | 2022 |
| Recurrent neural networks for time series forecasting: Current status and future directions | H Hewamalage, C Bergmeir, K Bandara. | 2021 |
| Improving the accuracy of global forecasting models using time series data augmentation | K Bandara, H Hewamalage, YH Liu, Y Kang, C Bergmeir. | 2021 |
| Neuralprophet: Explainable forecasting at scale | O Triebe, H Hewamalage, P Pilyugina, N Laptev, C Bergmeir, .... | 2021 |
| Time series extrinsic regression: Predicting numeric values from time series data | CW Tan, C Bergmeir, F Petitjean, GI Webb. | 2021 |
| Monash time series forecasting archive | R Godahewa, C Bergmeir, GI Webb, RJ Hyndman, P Montero-Manso. | 2021 |
| MSTL: A seasonal-trend decomposition algorithm for time series with multiple seasonal patterns | K Bandara, RJ Hyndman, C Bergmeir. | 2021 |
| Machine learning algorithms for predicting and risk profiling of cardiac surgery-associated acute kidney injury | JC Penny-Dimri, C Bergmeir, CM Reid, J Williams-Spence, AD Cochrane, .... | 2021 |
| Ensembles of localised models for time series forecasting | R Godahewa, K Bandara, GI Webb, S Smyl, C Bergmeir. | 2021 |
| SQAPlanner: Generating data-informed software quality improvement plans | D Rajapaksha, C Tantithamthavorn, J Jiarpakdee, C Bergmeir, J Grundy, .... | 2021 |
| MultiRocket: Effective summary statistics for convolutional outputs in time series classification | CW Tan, A Dempster, C Bergmeir, GI Webb. | 2021 |
| forecast: Forecasting functions for time series and linear models (Version 8.14) | R Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, .... | 2021 |
| Association between urine output and mortality in critically ill patients: a machine learning approach | AJ Heffernan, S Judge, SM Petrie, R Godahewa, C Bergmeir, D Pilcher, .... | 2021 |
| Versatile and robust transient stability assessment via instance transfer learning | S Meghdadi, G Tack, A Liebman, N Langrené, C Bergmeir. | 2021 |
| Causal inference using global forecasting models for counterfactual prediction | P Grecov, K Bandara, C Bergmeir, K Ackermann, S Campbell, D Scott, .... | 2021 |
| A look at the evaluation setup of the m5 forecasting competition | H Hewamalage, P Montero-Manso, C Bergmeir, RJ Hyndman. | 2021 |
| Dependency Learning Graph Neural Network for Multivariate Forecasting | A Patel, A Sriramulu, C Bergmeir, N Fourrier. | 2021 |
| Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach | K Bandara, C Bergmeir, S Smyl. | 2020 |
| Package ‘forecast’ | RJ Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, .... | 2020 |
| LSTM-MSNet: Leveraging forecasts on sets of related time series with multiple seasonal patterns | K Bandara, C Bergmeir, H Hewamalage. | 2020 |
| Package forecast: Forecasting functions for time series and linear models | RJ Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, .... | 2020 |
| forecast: Forecasting Functions for Time Series and Linear Models. 2020. R package version 8.12 | R Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, .... | 2020 |
| LoRMIkA: Local rule-based model interpretability with k-optimal associations | D Rajapaksha, C Bergmeir, W Buntine. | 2020 |
| Monash university, uea, ucr time series regression archive | CW Tan, C Bergmeir, F Petitjean, GI Webb. | 2020 |
| Towards accurate predictions and causal ‘what-if’analyses for planning and policy-making: a case study in emergency medical services demand | K Bandara, C Bergmeir, S Campbell, D Scott, D Lubman. | 2020 |
| A strong baseline for weekly time series forecasting | R Godahewa, C Bergmeir, GI Webb, P Montero-Manso. | 2020 |
| Time series regression | CW Tan, C Bergmeir, F Petitjean, GI Webb. | 2020 |
| Simulation and optimisation of air conditioning systems using machine learning | R Godahewa, C Deng, A Prouzeau, C Bergmeir. | 2020 |
| A comparison of characteristics and outcomes of patients admitted to the ICU with asthma in Australia and New Zealand and United states | H Abdelkarim, M Durie, R Bellomo, C Bergmeir, O Badawi, K El-Khawas, .... | 2020 |
| Monash University, UEA, UCR time series extrinsic regression archive | CW Tan, C Bergmeir, F Petitjean, GI Webb. | 2020 |
| Seasonal averaged one-dependence estimators: a novel algorithm to address seasonal concept drift in high-dimensional stream classification | R Godahewa, T Yann, C Bergmeir, F Petitjean. | 2020 |
| Package ‘Rmalschains’ | C Bergmeir, JM Benítez, D Molina, R Davies, D Eddelbuettel, N Hansen. | 2019 |
| 2018 Index IEEE Transactions on Cloud Computing Vol. 6 | CT Abdallah, K Ahmed, M Ali, A Almutairi, H Alshammari, .... | 2019 |
| ssc: An R Package for Semi-Supervised Classification | M González, O Rosado, JD Rodríguez, C Bergmeir, I Triguero, JM Benítez. | 2019 |
| 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. | 2019 |
| Machine learning applications in time series hierarchical forecasting | M Abolghasemi, RJ Hyndman, G Tarr, C Bergmeir. | 2019 |
| Forecasting functions for time series and linear models. 2019 | R Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, .... | 2019 |
| Closing the gap in surveillance and audit of invasive mold diseases for antifungal stewardship using machine learning | D Baggio, T Peel, AY Peleg, S Avery, M Prayaga, M Foo, G Haffari, M Liu, .... | 2019 |
| Multiobjective optimization for railway maintenance plans | D Peralta, C Bergmeir, M Krone, M Galende, M Menéndez, .... | 2018 |
| Self-labeling techniques for semi-supervised time series classification: an empirical study | M González, C Bergmeir, I Triguero, Y Rodríguez, JM Benítez. | 2018 |
| A note on the validity of cross-validation for evaluating autoregressive time series prediction | C Bergmeir, RJ Hyndman, B Koo. | 2018 |
| Exploring the sources of uncertainty: Why does bagging for time series forecasting work? | F Petropoulos, RJ Hyndman, C Bergmeir. | 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, .... | 2018 |
| Package forecast-the comprehensive R archive network | R Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, .... | 2018 |
| Package ‘Mcomp’ | R Hyndman, M Akram, C Bergmeir, M O'Hara-Wild, MR Hyndman. | 2018 |
| Comparison of characteristics and outcomes of patients admitted to the ICU with asthma in Australia, New Zealand and United States | H Abdelkarim, M Durie, K El-Khawas, R Bellomo, C Bergmeir, O Badawi. | 2018 |
| Deep learning based image analysis of fungal pneumonia in chest computed tomography in haematology patients | MR Ananda Rajah, T Tang, H Josh, S Ellis, A Kam, DK Varma, G Haffari, .... | 2018 |
| forecast: Forecasting functions for time series and linear models | RJ Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, .... | 2018 |
| Forecasting across time series databases using long short-term memory networks on groups of similar series | K Bandara, C Bergmeir, S Smyl. | 2017 |
| Designing a more efficient, effective and safe Medical Emergency Team (MET) service using data analysis | C Bergmeir, I Bilgrami, C Bain, GI Webb, J Orosz, D Pilcher. | 2017 |
| Modelling risk-adjusted variation in length of stay among Australian and New Zealand ICUs | LD Straney, AA Udy, A Burrell, C Bergmeir, S Huckson, DJ Cooper, .... | 2017 |
| Toward electronic surveillance of invasive mold diseases in hematology-oncology patients: An expert system combining natural language processing of chest computed tomography … | MR Ananda-Rajah, C Bergmeir, F Petitjean, MA Slavin, KA Thursky, .... | 2017 |
| Rsnns: neural networks in r using the Stuttgart neural network simulator (snns) | C Bergmeir, JM Benítez. | 2017 |
| Forecast: Forecasting functions for time series and linear models. R package | R Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, .... | 2017 |
| Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation | C Bergmeir, RJ Hyndman, JM Benítez. | 2016 |
| 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. | 2016 |
| A forecasting methodology for workload forecasting in cloud systems | FJ Baldán, S Ramírez-Gallego, C Bergmeir, F Herrera, JM Benítez. | 2016 |
| Memetic algorithms with local search chains in R: The Rmalschains package | CN Bergmeir, D Molina Cabrera, JM Benítez Sánchez. | 2016 |
| frbs: Fuzzy rule-based systems for classification and regression in R | LS Riza, CN Bergmeir, F Herrera, JM Benítez Sánchez. | 2015 |
| Package ‘RoughSets’ | LS Riza, A Janusz, D Slezak, C Cornelis, F Herrera, JM Benitez, .... | 2015 |
| Package ‘RSNNS’ | C Bergmeir, JM Benítez. | 2015 |
| Forecasting functions for time series and linear models | R Hyndman, G Athanasopoulos, C Bergmeir, G Caceres, L Chhay, .... | 2015 |
| A note on the validity of cross-validation for evaluating time series prediction. Monash University, Department of Econometrics and Business Statistics | C Bergmeir, RJ Hyndman, B Koo. | 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. | 2014 |
| On the usefulness of cross-validation for directional forecast evaluation | C Bergmeir, M Costantini, JM Benítez. | 2014 |
| Learning from data using the R package" FRBS" | LS Riza, C Bergmeir, F Herrera, JM Benitez. | 2014 |
| Package ‘frbs’ | LS Riza, C Bergmeir, F Herrera, JM Benitez. | 2014 |
| Constructing fuzzy rule-based systems with the R package “frbs” | LS Riza, C Bergmeir, F Herrera, JM Benítez. | 2014 |
| A study on the use of machine learning methods for incidence prediction in high-speed train tracks | C Bergmeir, G Sáinz, CM Bertrand, JM Benítez. | 2013 |
| Rsiopred: An R package for forecasting by exponential smoothing with model selection by a fuzzy multicriteria approach | C Bergmeir, JM Benítez, J Bermúdez, JV Segura, E Vercher. | 2013 |
| Actigraph GT3X: validation and determination of physical activity intensity cut points | A Santos-Lozano, F Santin-Medeiros, G Cardon, G Torres-Luque, .... | 2013 |
| Mcomp: Data from the M-competitions | RJ Hyndman, M Akram, C Bergmeir. | 2013 |
| New approaches in time series forecasting: methods, software and evaluation procedures | CN Bergmeir. | 2013 |
| Neural networks in R using the Stuttgart neural network simulator: RSNNS | CN Bergmeir, JM Benítez Sánchez. | 2012 |
| On the use of cross-validation for time series predictor evaluation | C Bergmeir, JM Benítez. | 2012 |
| 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. | 2012 |
| Time series modeling and forecasting using memetic algorithms for regime-switching models | C Bergmeir, I Triguero, D Molina, JL Aznarte, JM Benitez. | 2012 |
| Optimization of neuro-coefficient smooth transition autoregressive models using differential evolution | C Bergmeir, I Triguero, F Velasco, JM Benítez. | 2012 |
| 2012 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 23 | SP Adhikari, A Alessandri, B Alfano, AM Alimi, E Alonso, U Amato, .... | 2012 |
| Forecaster performance evaluation with cross-validation and variants | C Bergmeir, JM Benitez. | 2011 |
| Segmentation of cervical cell images using mean-shift filtering and morphological operators | C Bergmeir, MG Silvente, JE López-Cuervo, JM Benítez. | 2010 |
| Comparing calibration approaches for 3D ultrasound probes | C Bergmeir, M Seitel, C Frank, RD Simone, HP Meinzer, I Wolf. | 2009 |
| Operator guidance in 2D echocardiography via 3D model to image registration | C Bergmeir, N Subramanian. | 2009 |
| Klassifikation von Standardebenen in der 2D-Echokardiographie mittels 2D-3D-Bildregistrierung | C Bergmeir, N Subramanian. | 2009 |
| Entwicklung und Evaluation einer Kalibrierungsmethode für 3D-Ultraschall | C Bergmeir, M Seitel, C Frank, R De Simone, HP Meinzer, I Wolf. | 2008 |
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