Christoph Bergmeir

Category

PhD - Maria Zambrano

Contact

bergmeirmume4bloh@ugr.es

Organization

UGR

Researcher Profile

Total From 2019:
Citas Total: 9615 From 2019: 8289
Índice H Total: 37 From 2019: 34
Índice i10 Total: 74 From 2019: 67

Papers (139)

Title Authors Year
How Well Can Social Scientists Forecast Societal Change? I Grossmann, C Bergmeir, P Slattery. 2024
LLMs and Foundational Models: Not (Yet) as Good as Hoped. C Bergmeir. 2024
DeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series A Sriramulu, N Fourrier, C Bergmeir. 2024
Context Neural Networks: A Scalable Multivariate Model for Time Series Forecasting A Sriramulu, C Bergmeir, S Smyl. 2024
Counterfactual Predictions in Shared Markets: A Global Forecasting Approach with Deep Learning and Spillover Considerations P Grecov, K Ackermann, C Bergmeir. 2024
Predict. Optimize. Revise. On Forecast and Policy Stability in Energy Management Systems E Genov, J Ruddick, C Bergmeir, M Vafaeipour, T Coosemans, S Garcia, .... 2024
Fast Gibbs sampling for the local and global trend Bayesian exponential smoothing model X Long, DF Schmidt, C Bergmeir, S Smyl. 2024
Scalable Transformer for High Dimensional Multivariate Time Series Forecasting X Zhou, W Wang, W Buntine, S Qu, A Sriramulu, W Tan, C Bergmeir. 2024
Commentary: Can LLMs Provide Good Forecasts? C Bergmeir. 2024
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, .... 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
Environmental sound classification on the edge: Deep acoustic networks for extremely resource-constrained devices M Mohaimenuzzaman, C Bergmeir, IT West, B Meyer. 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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