Christoph Bergmeir

Management Science and Operations Research · 2012–2020

A researcher in the Science corpus: 10 papers; trusts 150 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Management Science and Operations Research — 284 citations into it; #1,462 of 11,946 by trust here, #1,613 by citations
• Economics and Econometrics — 265 citations into it; #14,740 of 23,481 by trust here, #18,597 by citations
• Artificial Intelligence — 173 citations into it; #20,121 of 35,345 by trust here, #21,316 by citations
• Statistics and Probability — 150 citations into it; #10,445 of 12,932 by trust here, #7,508 by citations
• Finance — 117 citations into it
• Signal Processing — 85 citations into it; #1,837 of 7,572 by trust here, #2,990 by citations
• Time-Series & Forecasting — 73 citations into it; #241 of 381 by trust here, #153 by citations
• Electrical and Electronic Engineering — 64 citations into it; #11,932 of 16,539 by trust here, #12,348 by citations

Whom Christoph Bergmeir trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Rob J Hyndman
2. Jeffrey L. Elman
3. Martin A. Riedmiller
4. Yann LeCun
5. Spyros Makridakis
6. Michael I. Jordan
7. Geoffrey E. Hinton
8. Yoshua Bengio
9. Trevor J. Hastie
10. Stephen Grossberg

Who trusts Christoph Bergmeir overall

1. Fotios Petropoulos
2. Vítor Cerqueira
3. Rob J Hyndman
4. Ron Wehrens
5. Brian Kenji Iwana
6. Luís Torgo
7. Kasun Bandara
8. Xiaodong Li
9. Momin M. Malik
10. Evangelos Spiliotis

Papers from Christoph Bergmeir's perspective

Ordered by how strongly the researchers Christoph Bergmeir trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation (2016) — 300 citations
• Exploring the sources of uncertainty: Why does bagging for time series forecasting work? (2018) — 143 citations
• A note on the validity of cross-validation for evaluating autoregressive time series prediction (2018) — 589 citations
• Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017) — 428 citations
• On the use of cross-validation for time series predictor evaluation (2012) — 1,121 citations
• Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions (2019) — 1,252 citations
• Forecasting: theory and practice (2020) — 551 citations
• forecast: Forecasting functions for time series and linear models (2018) — 1,092 citations
• Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017) — 32 citations
• Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS (2012) — 313 citations