Main research areas

Statistical Methods

  • Probabilistic Forecasting
  • High-dimensional methods
  • Time series: nonstationarity, nonlinearity, high-dimensionality, and count data
  • Non- and semiparametric methods
  • Network dependencies, extreme dependencies, quantiles
  • Generated regressors

Applied Econometrics

  • Financial Systemic risk measurement
  • Interdependencies of risks
  • Large dimensional extreme risk
  • Methods for high-frequency finance
  • Evaluation of policy measures
  • Forecasting of Cryptocurrencies and house prices

Epidemiology (YIG-PP Johannes Bracher)

  • probabilistic forecasting of infectious diseases
  • Covid-19 forecast hubs: systematic comparison, evaluation, ensemble building
  • Nowcasting of hospitalisations for Covid-19

Weather Forecasting (YIG Sebastian Lerch)

  • Machine learning methods for probabilistic forecasting and uncertainty quantification
  • Forecast verification and extreme events
  • Probabilistic weather forecasting: ensemble post-processing, subseasonal forecasts and hybrid models

Publications


2024
Guidance for the Effective Use of Business Intelligence and Analytics Systems. Dissertation
Gunklach, J.
2024, Oktober 9. Karlsruher Institut für Technologie (KIT)
Data Science-Based Analysis of Special Situations in Corporate Bonds. Dissertation
Baumann, F.
2024, April 9. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000169769
Generative machine learning methods for multivariate ensemble postprocessing
Chen, J.; Janke, T.; Steinke, F.; Lerch, S.
2024. The Annals of Applied Statistics, 18 (1), 159–183. doi:10.1214/23-AOAS1784
2023
Collaborative nowcasting of COVID-19 hospitalization incidences in Germany
Wolffram, D.; Abbott, S.; an der Heiden, M.; Funk, S.; Günther, F.; Hailer, D.; Heyder, S.; Hotz, T.; Bracher, J. E.; Schienle, M.; u. a.
2023. PLOS Computational Biology, 19 (8), Art.-Nr.: e1011394. doi:10.1371/journal.pcbi.1011394
Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States
Ray, E. L.; Brooks, L. C.; Bien, J.; Biggerstaff, M.; Bosse, N. I.; Bracher, J.; Cramer, E. Y.; Funk, S.; Gerding, A.; Johansson, M. A.; u. a.
2023. International Journal of Forecasting, 39 (3), 1366–1383. doi:10.1016/j.ijforecast.2022.06.005
Statistical Postprocessing of Numerical Weather Prediction Forecasts using Machine Learning. Dissertation
Schulz, B.
2023, Mai 25. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000158905
Model Diagnostics and Forecast Evaluation for Quantiles
Gneiting, T.; Wolffram, D.; Resin, J.; Kraus, K.; Bracher, J.; Dimitriadis, T.; Hagenmeyer, V.; Jordan, A. I.; Lerch, S.; Schienle, M.; u. a.
2023. Annual Review of Statistics and Its Application, 10. doi:10.1146/annurev-statistics-032921-020240
Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany
Brockhaus, E. K.; Wolffram, D.; Stadler, T.; Osthege, M.; Mitra, T.; Littek, J. M.; Krymova, E.; Klesen, A. J.; Huisman, J. S.; Bracher, J.; u. a.
2023. PLOS Computational Biology, 19 (11), Art.-Nr.: e1011653. doi:10.1371/journal.pcbi.1011653
Direction Augmentation in the Evaluation of Armed Conflict Predictions
Bracher, J.; Rüter, L.; Krüger, F.; Lerch, S.; Schienle, M.
2023. International Interactions, 49 (6), 989–1004. doi:10.1080/03050629.2023.2255923
The EUPPBench postprocessing benchmark dataset v1.0
Demaeyer, J.; Bhend, J.; Lerch, S.; Primo, C.; Van Schaeybroeck, B.; Atencia, A.; Ben Bouallègue, Z.; Chen, J.; Dabernig, M.; Horat, N.; u. a.
2023. Earth System Science Data, 15 (6), 2635–2653. doi:10.5194/essd-15-2635-2023
Scoring epidemiological forecasts on transformed scales
Bosse, N. I.; Abbott, S.; Cori, A.; van Leeuwen, E.; Bracher, J.; Funk, S.
2023. PLOS Computational Biology, 19 (8), Art.-Nr.: e1011393. doi:10.1371/journal.pcbi.1011393
Can Machine Learning Models be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal to Seasonal Timescales?
Kiefer, S. M.; Lerch, S.; Ludwig, P.; Pinto, J. G.
2023. Artificial Intelligence for the Earth Systems, 2 (4), Art.-Nr.: e230020. doi:10.1175/AIES-D-23-0020.1
Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations
Sherratt, K.; Gruson, H.; Grah, R.; Johnson, H.; Niehus, R.; Prasse, B.; Sandmann, F.; Deuschel, J.; Wolffram, D.; Bracher, J.; u. a.
2023. eLife, 12, Art.-Nr.: e81916. doi:10.7554/eLife.81916
Learning to Forecast: The Probabilistic Time Series Forecasting Challenge
Bracher, J.; Koster, N.; Krüger, F.; Lerch, S.
2023. The American Statistician, 1–13. doi:10.1080/00031305.2023.2199800
Comparison of multivariate post‐processing methods using global ECMWF ensemble forecasts
Lakatos, M.; Lerch, S.; Hemri, S.; Baran, S.
2023. Quarterly Journal of the Royal Meteorological Society, 149 (752), 856–877. doi:10.1002/qj.4436
2022
The United States COVID-19 Forecast Hub dataset
US COVID-19 Forecast Hub Consortium; Cramer, E. Y.; Huang, Y.; Wang, Y.; Ray, E. L.; Cornell, M.; Bracher, J.; Brennen, A.; Rivadeneira, A. J. C.; Wolffram, D.; u. a.
2022. Scientific Data, 9 (1), Art.-Nr.: 462. doi:10.1038/s41597-022-01517-w
Analysis of pesticide and persistent organic pollutant residues in German bats
Schanzer, S.; Koch, M.; Kiefer, A.; Jentke, T.; Veith, M.; Bracher, F.; Bracher, J.; Müller, C.
2022. Chemosphere, 305, Art.-Nr.: 135342. doi:10.1016/j.chemosphere.2022.135342
Collaborative Hubs: Making the Most of Predictive Epidemic Modeling
Reich, N. G.; Lessler, J.; Funk, S.; Viboud, C.; Vespignani, A.; Tibshirani, R. J.; Shea, K.; Schienle, M.; Runge, M. C.; Bracher, J.; u. a.
2022. American Journal of Public Health, 112 (6), 839–842. doi:10.2105/AJPH.2022.306831
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States
Cramer, E. Y.; Ray, E. L.; Lopez, V. K.; Bracher, J.; Brennen, A.; Castro Rivadeneira, A. J.; Gerding, A.; Gneiting, T.; House, K. H.; Huang, Y.; u. a.
2022. Proceedings of the National Academy of Sciences of the United States of America, 119 (15), e2113561119. doi:10.1073/pnas.2113561119
National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021
Bracher, J.; Wolffram, D.; Deuschel, J.; Görgen, K.; Ketterer, J. L.; Ullrich, A.; Abbott, S.; Barbarossa, M. V.; Bertsimas, D.; Schienle, M.; u. a.
2022. Communications Medicine, 2 (1), Art.-Nr.: 136. doi:10.1038/s43856-022-00191-8
Comparing human and model-based forecasts of COVID-19 in Germany and Poland
Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group; Bosse, N. I.; Abbott, S.; Bracher, J.; Hain, H.; Quilty, B. J.; Jit, M.; van Leeuwen, E.; Cori, A.; Funk, S.
2022. (J. M. McCaw, Hrsg.) PLOS Computational Biology, 18 (9), Art.Nr. e1010405. doi:10.1371/journal.pcbi.1010405
Large Spillover Networks of Nonstationary Systems
Chen, S.; Schienle, M.
2022. Journal of Business and Economic Statistics, 1–15. doi:10.1080/07350015.2022.2099870
Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing
Silini, R.; Lerch, S.; Mastrantonas, N.; Kantz, H.; Barreiro, M.; Masoller, C.
2022. Earth System Dynamics, 13 (3), 1157–1165. doi:10.5194/esd-13-1157-2022
Evaluating ensemble post‐processing for wind power forecasts
Phipps, K.; Lerch, S.; Andersson, M.; Mikut, R.; Hagenmeyer, V.; Ludwig, N.
2022. Wind Energy, 25 (8), 1379–1405. doi:10.1002/we.2736
Probabilistic Predictions from Deterministic Atmospheric River Forecasts with Deep Learning
Chapman, W. E.; Delle Monache, L.; Alessandrini, S.; Subramanian, A. C.; Ralph, F. M.; Xie, S.-P.; Lerch, S.; Hayatbini, N.
2022. Monthly Weather Review, 150 (1), 215–234. doi:10.1175/MWR-D-21-0106.1
2021
A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave
Bracher, J.; Wolffram, D.; Deuschel, J.; Görgen, K.; Ketterer, J. L.; Ullrich, A.; Abbott, S.; Barbarossa, M. V.; Bertsimas, D.; Schienle, M.; u. a.
2021. (List of Contributors by Team, Hrsg.) Nature communications, 12 (1), 5173. doi:10.1038/s41467-021-25207-0
Evaluating epidemic forecasts in an interval format
Bracher, J.; Ray, E. L.; Gneiting, T.; Reich, N. G.
2021. (V. E. Pitzer, Hrsg.) PLoS Computational Biology, 17 (2), Art.Nr. e1008618. doi:10.1371/JOURNAL.PCBI.1008618
Statistical postprocessing for weather forecasts review, challenges, and avenues in a big data world
Vannitsem, S.; Bremnes, J. B.; Demaeyer, J.; Evans, G. R.; Flowerdew, J.; Hemri, S.; Lerch, S.; Roberts, N.; Theis, S.; Atencia, A.; u. a.
2021. Bulletin of the American Meteorological Society, 102 (3), E681-E699. doi:10.1175/BAMS-D-19-0308.1
Machine learning for total cloud cover prediction
Baran, Á.; Lerch, S.; El Ayari, M.; Baran, S.
2021. Neural computing & applications, 33, 2605–2620. doi:10.1007/s00521-020-05139-4
2020
Preface: Advances in post-processing and blending of deterministic and ensemble forecasts
Hemri, S.; Lerch, S.; Taillardat, M.; Vannitsem, S.; Wilks, D. S.
2020. Nonlinear processes in geophysics, 27 (4), 519–521. doi:10.5194/npg-27-519-2020
Predictive Inference Based on Markov Chain Monte Carlo Output
Krüger, F.; Lerch, S.; Thorarinsdottir, T.; Gneiting, T.
2020. International statistical review, 89 (2), 274–301. doi:10.1111/insr.12405
Simulation-based comparison of multivariate ensemble post-processing methods
Lerch, S.; Baran, S.; Möller, A.; Groß, J.; Schefzik, R.; Hemri, S.; Graeter, M.
2020. Nonlinear processes in geophysics, 27 (2), 349–371. doi:10.5194/npg-27-349-2020
Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression
Lang, M. N.; Lerch, S.; Mayr, G. J.; Simon, T.; Stauffer, R.; Zeileis, A.
2020. Nonlinear processes in geophysics, 27 (1), 23–34. doi:10.5194/npg-27-23-2020
A retrospective assessment of different endodontic treatment protocols
Bartols, A.; Bormann, C.; Werner, L.; Schienle, M.; Walther, W.; Dörfer, C. E.
2020. PeerJ, 8, e8495. doi:10.7717/peerj.8495
Detecting Structural Differences in Tail Dependence of Financial Time Series
Bormann, C.; Schienle, M.
2020. Journal of business & economic statistics, 38 (2), 380–392. doi:10.1080/07350015.2018.1506343
2019
Determination of vector error correction models in high dimensions
Liang, C.; Schienle, M.
2019. Journal of econometrics, 208 (2), 418–441. doi:10.1016/j.jeconom.2018.09.018
Evaluating Probabilistic Forecasts with
Jordan, A.; Krüger, F.; Lerch, S.
2019. Journal of statistical software, 90 (12), 1–37. doi:10.18637/jss.v090.i12
Testing for an Omitted Multiplicative Long-Term Component in GARCH Models
Conrad, C.; Schienle, M.
2019. Journal of business & economic statistics, 38 (2), 229–242. doi:10.1080/07350015.2018.1482759
Measuring connectedness of euro area sovereign risk
Buse, R.; Schienle, M.
2019. International journal of forecasting, 35 (1), 25–44. doi:10.1016/j.ijforecast.2018.07.010
2018
Neural Networks for Postprocessing Ensemble Weather Forecasts
Rasp, S.; Lerch, S.
2018. Monthly weather review, 146 (11), 3885–3900. doi:10.1175/MWR-D-18-0187.1
High Dimensional Time Series — New Techniques and Applications. Dissertation
Liang, C.
2018. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000084976
2017
Forecaster’s dilemma: Extreme events and forecast evaluation
Lerch, S.; Thorarinsdottir, T. L.; Ravazzolo, F.; Gneiting, T.
2017. Statistical science, 32 (1), 106–127. doi:10.1214/16-STS588
Similarity-based semilocal estimation of post-processing models
Lerch, S.; Baran, S.
2017. Journal of the Royal Statistical Society / C, 66 (1), 29–51. doi:10.1111/rssc.12153
2016
Mixture EMOS model for calibrating ensemble forecasts of wind speed
Baran, S.; Lerch, S.
2016. Environmetrics, 27 (2), 116–130. doi:10.1002/env.2380
Systemic risk spillovers in the European banking and sovereign network
Betz, F.; Hautsch, N.; Peltonen, T. A.; Schienle, M.
2016. Journal of financial stability, 25, 206–224. doi:10.1016/j.jfs.2015.10.006
Semiparametric Estimation with Generated Covariates
Mammen, E.; Rothe, C.; Schienle, M.
2016. Econometric theory, 32 (5), 1140–1177. doi:10.1017/S0266466615000134
2015
Beyond Dimension two: A Test for Higher-Order Tail Risk
Bormann, C.; Schaumburg, J.; Schienle, M.
2015. Journal of financial econometrics, 14 (3), 552–580. doi:10.1093/jjfinec/nbv022
Financial Network Systemic Risk Contributions
Hautsch, N.; Schaumburg, J.; Schienle, M.
2015. Review of finance, 19 (2), 685–738. doi:10.1093/rof/rfu010
2014
Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes
Hautsch, N.; Malec, P.; Schienle, M.
2014. Journal of financial econometrics, 12 (1), 89–121. doi:10.1093/jjfinec/nbt002
Systemic Risk Spillovers in the European Banking and Sovereign Network
Betz, F.; Hautsch, N.; Peltonen, T.; Schienle, M.
2014. Univ.-Bibliothek Frankfurt am Main. doi:10.2139/ssrn.2504400
Additive Models: Extensions and Related Models
Mammen, E.; Park, B. U.; Schienle, M.
2014. The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics. Ed.: J.Racine, Oxford Univ. Press. doi:10.1093/oxfordhb/9780199857944.013.007
Forecasting systemic impact in financial networks
Hautsch, N.; Schaumburg, J.; Schienle, M.
2014. International Journal of Forecasting, 30 (3), 781–794. doi:10.1016/j.ijforecast.2013.09.004
Nonparametric Kernel Density Estimation Near the Boundary
Malec, P.; Schienle, M.
2014. Computational Statistics and Data Analysis, 72, 57–76. doi:10.1016/j.csda.2013.10.023
2012
Nonparametric regression with nonparametrically generated covariates
Mammen, E.; Rothe, C.; Schienle, M.
2012. The annals of statistics, 40 (2), 1132–1170. doi:10.1214/12-AOS995