From 9c90536f9f852cea58eb84acdb7abd8e104af79c Mon Sep 17 00:00:00 2001 From: Jonathan Berrisch Date: Sat, 31 May 2025 12:08:20 +0200 Subject: [PATCH] Add overview slides and cleanup bib --- assets/library.bib | 607 ++++++------------------------------ custom.scss | 7 + index.qmd | 281 +++++++++++++---- remove_unused_bibentries.sh | 53 ++++ 4 files changed, 378 insertions(+), 570 deletions(-) create mode 100755 remove_unused_bibentries.sh diff --git a/assets/library.bib b/assets/library.bib index 371ac6e..0ead3b1 100644 --- a/assets/library.bib +++ b/assets/library.bib @@ -1,27 +1,17 @@ -@article{aastveit2014nowcasting, - title = {Nowcasting GDP in real time: A density combination approach}, - author = {Aastveit, Knut Are and Gerdrup, Karsten R and Jore, Anne Sofie and Thorsrud, Leif Anders}, - journal = {Journal of Business \& Economic Statistics}, - volume = {32}, - number = {1}, - pages = {48--68}, - year = {2014}, - publisher = {Taylor \& Francis} -} @article{berrisch2023modeling, title = {Modeling volatility and dependence of European carbon and energy prices}, - author = {Berrisch, Jonathan and Pappert, Sven and Ziel, Florian and Arsova, Antonia}, + author = {Jonathan Berrisch and Sven Pappert and Florian Ziel and Antonia Arsova}, + year = {2023}, + month = {3}, journal = {Finance Research Letters}, + publisher = {Elsevier BV}, volume = {52}, pages = {103503}, - year = {2023}, - publisher = {Elsevier} -} -@incollection{aastveit2019evolution, - title = {The Evolution of Forecast Density Combinations in Economics}, - author = {Aastveit, Knut Are and Mitchell, James and Ravazzolo, Francesco and van Dijk, Herman K}, - booktitle = {Oxford Research Encyclopedia of Economics and Finance}, - year = {2019} + doi = {10.1016/j.frl.2022.103503}, + issn = {1544-6123}, + url = {https://arxiv.org/pdf/2208.14311}, + abstract = {We study the prices of European Emission Allowances (EUA), whereby we analyze their uncertainty and dependencies on related energy prices (natural gas, coal, and oil). We propose a probabilistic multivariate conditional time series model with a VECM-Copula-GARCH structure which exploits key characteristics of the data. Data are normalized with respect to inflation and carbon emissions to allow for proper cross-series evaluation. The forecasting performance is evaluated in an extensive rolling-window forecasting study, covering eight years out-of-sample. We discuss our findings for both levels- and log-transformed data, focusing on time-varying correlations, and in view of the Russian invasion of Ukraine.}, + keywords = {Carbon prices, Conditional volatility, Copula, Emission allowances, Energy markets, Forecasting, Multivariate modeling, Time series} } @article{marcjasz2022distributional, title = {Distributional neural networks for electricity price forecasting}, @@ -33,528 +23,66 @@ doi = {10.1016/j.eneco.2023.106843}, publisher = {Elsevier} } -@article{atiya2020does, - title = {Why does forecast combination work so well?}, - author = {Atiya, Amir F}, - journal = {International Journal of Forecasting}, - volume = {36}, - number = {1}, - pages = {197--200}, - year = {2020}, - publisher = {Elsevier} -} -@article{atsalakis2016using, - title = {Using computational intelligence to forecast carbon prices}, - author = {Atsalakis, George S}, - journal = {Applied Soft Computing}, - volume = {43}, - pages = {107--116}, - year = {2016}, - publisher = {Elsevier} -} -@article{bai2020does, - title = {Does crude oil futures price really help to predict spot oil price? New evidence from density forecasting}, - author = {Bai, Lan and Li, Xiafei and Wei, Yu and Wei, Guiwu}, - journal = {International Journal of Finance \& Economics}, - year = {2020}, - publisher = {Wiley Online Library} -} -@article{benz2009modeling, - title = {Modeling the price dynamics of CO2 emission allowances}, - author = {Benz, Eva and Tr{\"u}ck, Stefan}, - journal = {Energy Economics}, - volume = {31}, - number = {1}, - pages = {4--15}, - year = {2009}, - publisher = {Elsevier} -} -@article{biau2011sequential, - title = {Sequential quantile prediction of time series}, - author = {Biau, G{\'e}rard and Patra, Beno{\^\i}t}, - journal = {IEEE Transactions on Information Theory}, - volume = {57}, - number = {3}, - pages = {1664--1674}, - year = {2011}, - publisher = {IEEE} -} -@inproceedings{bousquet2001tracking, - title = {Tracking a small set of experts by mixing past posteriors}, - author = {Bousquet, Olivier and Warmuth, Manfred K}, - booktitle = {International Conference on Computational Learning Theory}, - pages = {31--47}, - year = {2001}, - organization = {Springer} -} -@article{bregere2020online, - title = {Online hierarchical forecasting for power consumption data}, - author = {Br{\'e}g{\`e}re, Margaux and Huard, Malo}, - journal = {arXiv preprint arXiv:2003.00585}, - year = {2020} -} -@article{busetti2017quantile, - title = {Quantile aggregation of density forecasts}, - author = {Busetti, Fabio}, - journal = {Oxford Bulletin of Economics and Statistics}, - volume = {79}, - number = {4}, - pages = {495--512}, - year = {2017}, - publisher = {Wiley Online Library} -} @book{cesa2006prediction, - title = {Prediction, learning, and games}, - author = {Cesa-Bianchi, Nicolo and Lugosi, G{\'a}bor}, + title = {{Prediction, learning, and games}}, + author = {Cesa-Bianchi, Nicol{\`o} and Lugosi, G{\'a}bor}, year = {2006}, - publisher = {Cambridge university press} -} -@article{cesa2012mirror, - title = {Mirror descent meets fixed share (and feels no regret)}, - author = {Cesa-Bianchi, Nicolo and Gaillard, Pierre and Lugosi, G{\'a}bor and Stoltz, Gilles}, - journal = {Advances in Neural Information Processing Systems}, - volume = {25}, - pages = {980--988}, - year = {2012} -} -@article{cheng2015forecasting, - title = {Forecasting with factor-augmented regression: A frequentist model averaging approach}, - author = {Cheng, Xu and Hansen, Bruce E}, - journal = {Journal of Econometrics}, - volume = {186}, - number = {2}, - pages = {280--293}, - year = {2015}, - publisher = {Elsevier} -} -@article{chernozhukov2010quantile, - title = {Quantile and probability curves without crossing}, - author = {Chernozhukov, Victor and Fern{\'a}ndez-Val, Iv{\'a}n and Galichon, Alfred}, - journal = {Econometrica}, - volume = {78}, - number = {3}, - pages = {1093--1125}, - year = {2010}, - publisher = {Wiley Online Library} -} -@article{devaine2013forecasting, - title = {Forecasting electricity consumption by aggregating specialized experts}, - author = {Devaine, Marie and Gaillard, Pierre and Goude, Yannig and Stoltz, Gilles}, - journal = {Machine Learning}, - volume = {90}, - number = {2}, - pages = {231--260}, - year = {2013}, - publisher = {Springer} -} -@article{dutta2018modeling, - title = {Modeling and forecasting the volatility of carbon emission market: The role of outliers, time-varying jumps and oil price risk}, - author = {Dutta, Anupam}, - journal = {Journal of Cleaner Production}, - volume = {172}, - pages = {2773--2781}, - year = {2018}, - publisher = {Elsevier} -} -@article{eddelbuettel2014rcpparmadillo, - title = {RcppArmadillo: Accelerating R with high-performance C++ linear algebra}, - author = {Eddelbuettel, Dirk and Sanderson, Conrad}, - journal = {Computational Statistics \& Data Analysis}, - volume = {71}, - pages = {1054--1063}, - year = {2014}, - publisher = {Elsevier} -} -@article{fragoso2018bayesian, - title = {Bayesian model averaging: A systematic review and conceptual classification}, - author = {Fragoso, Tiago M and Bertoli, Wesley and Louzada, Francisco}, - journal = {International Statistical Review}, - volume = {86}, - number = {1}, - pages = {1--28}, - year = {2018}, - publisher = {Wiley Online Library} -} -@inproceedings{gaillard2014second, - title = {A second-order bound with excess losses}, - author = {Gaillard, Pierre and Stoltz, Gilles and Van Erven, Tim}, - booktitle = {Conference on Learning Theory}, - pages = {176--196}, - year = {2014}, - organization = {PMLR} -} -@incollection{gaillard2015forecasting, - title = {Forecasting electricity consumption by aggregating experts; how to design a good set of experts}, - author = {Gaillard, Pierre and Goude, Yannig}, - booktitle = {Modeling and stochastic learning for forecasting in high dimensions}, - pages = {95--115}, - year = {2015}, - publisher = {Springer} + month = {3}, + publisher = {Cambridge university press}, + pages = {I--XII, 1--394}, + doi = {10.1017/CBO9780511546921}, + isbn = {978-0521841085} } @inproceedings{gaillard2018efficient, - title = {Efficient online algorithms for fast-rate regret bounds under sparsity}, + title = {{Efficient online algorithms for fast-rate regret bounds under sparsity}}, author = {Gaillard, Pierre and Wintenberger, Olivier}, - booktitle = {Advances in Neural Information Processing Systems}, + year = {2018}, + month = {5}, + journal = {arXiv (Cornell University)}, + booktitle = {Proceedings of the 32nd International Conference on Neural Information Processing Systems}, + publisher = {Cornell University}, pages = {7026--7036}, - year = {2018} -} -@article{garcia2020short, - title = {Short-term European Union Allowance price forecasting with artificial neural networks}, - author = {Garc{\'\i}a, Agust{\'\i}n and Jaramillo-Mor{\'a}n, Miguel A}, - journal = {Entrepreneurship and Sustainability Issues}, - volume = {8}, - number = {1}, - pages = {261}, - year = {2020} + doi = {10.48550/arxiv.1805.09174}, + url = {https://arxiv.org/abs/1805.09174}, + editor = {Bengio, Samy and Wallach, Hanna M. and Larochelle, Hugo and Grauman, Kristen and Cesa-Bianchi, Nicolò and Garnett, Roman} } @article{gneiting2007strictly, - title = {Strictly proper scoring rules, prediction, and estimation}, + title = {{Strictly proper scoring rules, prediction, and estimation}}, author = {Gneiting, Tilmann and Raftery, Adrian E}, + year = {2007}, + month = {3}, journal = {Journal of the American statistical Association}, + publisher = {Taylor \& Francis}, volume = {102}, number = {477}, pages = {359--378}, - year = {2007}, - publisher = {Taylor \& Francis} -} -@article{gneiting2011comparing, - title = {Comparing density forecasts using threshold-and quantile-weighted scoring rules}, - author = {Gneiting, Tilmann and Ranjan, Roopesh}, - journal = {Journal of Business \& Economic Statistics}, - volume = {29}, - number = {3}, - pages = {411--422}, - year = {2011}, - publisher = {Taylor \& Francis} + doi = {10.1198/016214506000001437}, + issn = {0162-1459} } @article{gneiting2011making, - title = {Making and evaluating point forecasts}, + title = {{Making and evaluating point forecasts}}, author = {Gneiting, Tilmann}, + year = {2011}, + month = {6}, journal = {Journal of the American Statistical Association}, + publisher = {Taylor \& Francis}, volume = {106}, number = {494}, pages = {746--762}, - year = {2011}, - publisher = {Taylor \& Francis} -} -@article{gneiting2011quantiles, - title = {Quantiles as optimal point forecasts}, - author = {Gneiting, Tilmann}, - journal = {International Journal of forecasting}, - volume = {27}, - number = {2}, - pages = {197--207}, - year = {2011}, - publisher = {Elsevier} -} -@article{hansen2008least, - title = {Least-squares forecast averaging}, - author = {Hansen, Bruce E}, - journal = {Journal of Econometrics}, - volume = {146}, - number = {2}, - pages = {342--350}, - year = {2008}, - publisher = {Elsevier} -} -@article{hao2020modelling, - title = {Modelling of carbon price in two real carbon trading markets}, - author = {Hao, Yan and Tian, Chengshi and Wu, Chunying}, - journal = {Journal of Cleaner Production}, - volume = {244}, - pages = {118556}, - year = {2020}, - publisher = {Elsevier} -} -@article{he1997quantile, - title = {Quantile curves without crossing}, - author = {He, Xuming}, - journal = {The American Statistician}, - volume = {51}, - number = {2}, - pages = {186--192}, - year = {1997}, - publisher = {Taylor \& Francis} -} -@article{herbster1998tracking, - title = {Tracking the best expert}, - author = {Herbster, Mark and Warmuth, Manfred K}, - journal = {Machine learning}, - volume = {32}, - number = {2}, - pages = {151--178}, - year = {1998}, - publisher = {Springer} -} -@article{hsiao2014there, - title = {Is there an optimal forecast combination?}, - author = {Hsiao, Cheng and Wan, Shui Ki}, - journal = {Journal of Econometrics}, - volume = {178}, - pages = {294--309}, - year = {2014}, - publisher = {Elsevier} -} -@book{hyndman2018forecasting, - title = {Forecasting: principles and practice}, - author = {Hyndman, Rob J and Athanasopoulos, George}, - year = {2018}, - publisher = {OTexts} -} -@article{jore2010combining, - title = {Combining forecast densities from VARs with uncertain instabilities}, - author = {Jore, Anne Sofie and Mitchell, James and Vahey, Shaun P}, - journal = {Journal of Applied Econometrics}, - volume = {25}, - number = {4}, - pages = {621--634}, - year = {2010}, - publisher = {Wiley Online Library} -} -@inproceedings{kakade2008generalization, - title = {On the Generalization Ability of Online Strongly Convex Programming Algorithms.}, - author = {Kakade, Sham M and Tewari, Ambuj}, - booktitle = {NIPS}, - pages = {801--808}, - year = {2008} -} -@article{kapetanios2015generalised, - title = {Generalised density forecast combinations}, - author = {Kapetanios, G and Mitchell, James and Price, Simon and Fawcett, Nicholas}, - journal = {Journal of Econometrics}, - volume = {188}, - number = {1}, - pages = {150--165}, - year = {2015}, - publisher = {Elsevier} -} -@inproceedings{koolen2015second, - title = {Second-order quantile methods for experts and combinatorial games}, - author = {Koolen, Wouter M and Van Erven, Tim}, - booktitle = {Conference on Learning Theory}, - pages = {1155--1175}, - year = {2015} -} -@article{koop2013forecasting, - title = {Forecasting the European carbon market}, - author = {Koop, Gary and Tole, Lise}, - journal = {Journal of the Royal Statistical Society: Series A (Statistics in Society)}, - volume = {176}, - number = {3}, - pages = {723--741}, - year = {2013}, - publisher = {Wiley Online Library} -} -@article{korotin2019integral, - title = {Integral Mixabilty: a Tool for Efficient Online Aggregation of Functional and Probabilistic Forecasts}, - author = {Korotin, Alexander and V'yugin, Vladimir and Burnaev, Evgeny}, - journal = {arXiv preprint arXiv:1912.07048}, - year = {2019} -} -@inproceedings{korotin2020mixing, - title = {Mixing past predictions}, - author = {Korotin, Alexander and V’yugin, Vladimir and Burnaev, Evgeny}, - booktitle = {Conformal and Probabilistic Prediction and Applications}, - pages = {171--188}, - year = {2020}, - organization = {PMLR} -} -@article{lichtendahl2013better, - title = {Is it better to average probabilities or quantiles?}, - author = {Lichtendahl Jr, Kenneth C and Grushka-Cockayne, Yael and Winkler, Robert L}, - journal = {Management Science}, - volume = {59}, - number = {7}, - pages = {1594--1611}, - year = {2013}, - publisher = {INFORMS} -} -@article{lin2018multi, - title = {A multi-model combination approach for probabilistic wind power forecasting}, - author = {Lin, You and Yang, Ming and Wan, Can and Wang, Jianhui and Song, Yonghua}, - journal = {IEEE Transactions on Sustainable Energy}, - volume = {10}, - number = {1}, - pages = {226--237}, - year = {2018}, - publisher = {IEEE} -} -@article{littlestone1994weighted, - title = {The weighted majority algorithm}, - author = {Littlestone, Nick and Warmuth, Manfred K}, - journal = {Information and computation}, - volume = {108}, - number = {2}, - pages = {212--261}, - year = {1994}, - publisher = {Elsevier} -} -@article{lu2015jackknife, - title = {Jackknife model averaging for quantile regressions}, - author = {Lu, Xun and Su, Liangjun}, - journal = {Journal of Econometrics}, - volume = {188}, - number = {1}, - pages = {40--58}, - year = {2015}, - publisher = {Elsevier} -} -@article{maciejowska2020pca, - title = {PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices}, - author = {Maciejowska, Katarzyna and Uniejewski, Bartosz and Serafin, Tomasz}, - journal = {Energies}, - volume = {13}, - number = {14}, - pages = {3530}, - year = {2020}, - publisher = {Multidisciplinary Digital Publishing Institute} -} -@article{mhammedi2019lipschitz, - title = {Lipschitz adaptivity with multiple learning rates in online learning}, - author = {Mhammedi, Zakaria and Koolen, Wouter M and Van Erven, Tim}, - journal = {arXiv preprint arXiv:1902.10797}, - year = {2019} -} -@article{nowotarski2018recent, - title = {Recent advances in electricity price forecasting: A review of probabilistic forecasting}, - author = {Nowotarski, Jakub and Weron, Rafa{\l}}, - journal = {Renewable and Sustainable Energy Reviews}, - volume = {81}, - pages = {1548--1568}, - year = {2018}, - publisher = {Elsevier} -} -@article{opschoor2017combining, - title = {Combining density forecasts using focused scoring rules}, - author = {Opschoor, Anne and Van Dijk, Dick and van der Wel, Michel}, - journal = {Journal of Applied Econometrics}, - volume = {32}, - number = {7}, - pages = {1298--1313}, - year = {2017}, - publisher = {Wiley Online Library} -} -@article{petropoulos2020forecasting, - title = {Forecasting: theory and practice}, - author = {Petropoulos, Fotios and Apiletti, Daniele and Assimakopoulos, Vassilios and Babai, Mohamed Zied and Barrow, Devon K and Bergmeir, Christoph and Bessa, Ricardo J and Boylan, John E and Browell, Jethro and Carnevale, Claudio and others}, - journal = {arXiv preprint arXiv:2012.03854}, - year = {2020} -} -@article{raftery2005using, - title = {Using Bayesian model averaging to calibrate forecast ensembles}, - author = {Raftery, Adrian E and Gneiting, Tilmann and Balabdaoui, Fadoua and Polakowski, Michael}, - journal = {Monthly weather review}, - volume = {133}, - number = {5}, - pages = {1155--1174}, - year = {2005} -} -@article{segnon2017modeling, - title = {Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models}, - author = {Segnon, Mawuli and Lux, Thomas and Gupta, Rangan}, - journal = {Renewable and Sustainable Energy Reviews}, - volume = {69}, - pages = {692--704}, - year = {2017}, - publisher = {Elsevier} -} -@article{thorey2017online, - title = {Online learning with the Continuous Ranked Probability Score for ensemble forecasting}, - author = {Thorey, Jean and Mallet, Vivien and Baudin, Paul}, - journal = {Quarterly Journal of the Royal Meteorological Society}, - volume = {143}, - number = {702}, - pages = {521--529}, - year = {2017}, - publisher = {Wiley Online Library} -} -@article{thorey2018ensemble, - title = {Ensemble forecast of photovoltaic power with online CRPS learning}, - author = {Thorey, Jean and Chaussin, Christophe and Mallet, Vivien}, - journal = {International Journal of Forecasting}, - volume = {34}, - number = {4}, - pages = {762--773}, - year = {2018}, - publisher = {Elsevier} -} -@article{tu2011markowitz, - title = {Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies}, - author = {Tu, Jun and Zhou, Guofu}, - journal = {Journal of Financial Economics}, - volume = {99}, - number = {1}, - pages = {204--215}, - year = {2011}, - publisher = {Elsevier} -} -@article{v2020online, - title = {Online Aggregation of Probabilistic Forecasts Based on the Continuous Ranked Probability Score}, - author = {V’yugin, VV and Trunov, VG}, - journal = {Journal of Communications Technology and Electronics}, - volume = {65}, - number = {6}, - pages = {662--676}, - year = {2020}, - publisher = {Springer} -} -@article{van2018probabilistic, - title = {Probabilistic forecasting of solar power, electricity consumption and net load: Investigating the effect of seasons, aggregation and penetration on prediction intervals}, - author = {Van der Meer, DW and Munkhammar, Joakim and Wid{\'e}n, Joakim}, - journal = {Solar Energy}, - volume = {171}, - pages = {397--413}, - year = {2018}, - publisher = {Elsevier} -} -@article{vovk1990aggregating, - title = {Aggregating strategies}, - author = {Vovk, Volodimir G}, - journal = {Proc. of Computational Learning Theory, 1990}, - year = {1990} -} -@book{wahba1990spline, - title = {Spline models for observational data}, - author = {Wahba, Grace}, - year = {1990}, - publisher = {SIAM} -} -@book{wang2011smoothing, - title = {Smoothing splines: methods and applications}, - author = {Wang, Yuedong}, - year = {2011}, - publisher = {CRC Press} -} -@article{wang2019jackknife, - title = {Jackknife Model Averaging for Composite Quantile Regression}, - author = {Wang, Miaomiao and Zou, Guohua}, - journal = {arXiv preprint arXiv:1910.12209}, - year = {2019} + doi = {10.1198/jasa.2011.r10138}, + issn = {0162-1459}, + url = {https://arxiv.org/pdf/0912.0902.pdf} } @article{wintenberger2017optimal, title = {Optimal learning with Bernstein online aggregation}, author = {Wintenberger, Olivier}, + year = {2017}, journal = {Machine Learning}, + publisher = {Springer}, volume = {106}, number = {1}, pages = {119--141}, - year = {2017}, - publisher = {Springer} -} -@article{zamo2020sequential, - title = {Sequential Aggregation of Probabilistic Forecasts--Applicaton to Wind Speed Ensemble Forecasts}, - author = {Zamo, Micha{\"e}l and Bel, Liliane and Mestre, Olivier}, - journal = {arXiv preprint arXiv:2005.03540}, - year = {2020} -} -@article{zhang2020load, - title = {Load probability density forecasting by transforming and combining quantile forecasts}, - author = {Zhang, Shu and Wang, Yi and Zhang, Yutian and Wang, Dan and Zhang, Ning}, - journal = {Applied Energy}, - volume = {277}, - pages = {115600}, - year = {2020}, - publisher = {Elsevier} + doi = {10.1007/s10994-016-5592-6} } @article{BERRISCH2023105221, title = {CRPS learning}, @@ -587,4 +115,57 @@ issn = {0169-2070}, abstract = {This paper presents a new method for combining (or aggregating or ensembling) multivariate probabilistic forecasts, considering dependencies between quantiles and marginals through a smoothing procedure that allows for online learning. We discuss two smoothing methods: dimensionality reduction using Basis matrices and penalized smoothing. The new online learning algorithm generalizes the standard CRPS learning framework into multivariate dimensions. It is based on Bernstein Online Aggregation (BOA) and yields optimal asymptotic learning properties. The procedure uses horizontal aggregation, i.e., aggregation across quantiles. We provide an in-depth discussion on possible extensions of the algorithm and several nested cases related to the existing literature on online forecast combination. We apply the proposed methodology to forecasting day-ahead electricity prices, which are 24-dimensional distributional forecasts. The proposed method yields significant improvements over uniform combination in terms of continuous ranked probability score (CRPS). We discuss the temporal evolution of the weights and hyperparameters and present the results of reduced versions of the preferred model. A fast C＋＋implementation of the proposed algorithm is provided in the open-source R-Package profoc on CRAN.}, keywords = {Combination, Aggregation, Ensembling, Online, Multivariate, Probabilistic, Forecasting, Quantile, Time series, Distribution, Density, Prediction, Splines} +} +@article{berrisch2025rcpptimer, + title = {rcpptimer: Rcpp Tic-Toc Timer with OpenMP Support}, + author = {Berrisch, Jonathan}, + year = {2025}, + month = {1}, + journal = {arXiv preprint arXiv:2501.15856}, + publisher = {Cornell University}, + volume = {abs/2501.15856}, + doi = {10.48550/arXiv.2501.15856}, + issn = {2331-8422}, + url = {https://arxiv.org/pdf/2501.15856} +} +@article{hirsch2024online, + title = {Online Distributional Regression}, + author = {Hirsch, Simon and Berrisch, Jonathan and Ziel, Florian}, + year = {2024}, + month = {6}, + journal = {arXiv preprint arXiv:2407.08750}, + publisher = {Cornell University}, + volume = {abs/2407.08750}, + doi = {10.48550/arXiv.2407.08750}, + issn = {2331-8422}, + url = {https://arxiv.org/pdf/2407.08750} +} +@article{berrisch2022distributional, + title = {Distributional modeling and forecasting of natural gas prices}, + author = {Berrisch, Jonathan and Ziel, Florian}, + year = {2022}, + month = {1}, + journal = {Journal of Forecasting}, + publisher = {Wiley}, + volume = {41}, + number = {6}, + pages = {1065--1086}, + doi = {10.1002/for.2853}, + issn = {0277-6693}, + url = {https://arxiv.org/pdf/2010.06227}, + abstract = {Abstract We examine the problem of modeling and forecasting European day-ahead and month-ahead natural gas prices. For this, we propose two distinct probabilistic models that can be utilized in risk and portfolio management. We use daily pricing data ranging from 2011 to 2020. Extensive descriptive data analysis shows that both time series feature heavy tails and conditional heteroscedasticity and show asymmetric behavior in their differences. We propose state-space time series models under skewed, heavy-tailed distributions to capture all stylized facts of the data. They include the impact of autocorrelation, seasonality, risk premia, temperature, storage levels, the price of European Emission Allowances, and related fuel prices of oil, coal, and electricity. We provide rigorous model diagnostics and interpret all model components in detail. Additionally, we conduct a probabilistic forecasting study with significance tests and compare the predictive performance against literature benchmarks. The proposed day-ahead (month-ahead) model leads to a 13\% (9\%) reduction in out-of-sample continuous ranked probability score (CRPS) compared with the best performing benchmark model, mainly due to adequate modeling of the volatility and heavy tails.}, + keywords = {gas prices, heavy-tailed distribution, probabilistic forecasting, risk premium, state-space models} +} +@article{BERRISCH2023100236, + title = {High-resolution peak demand estimation using generalized additive models and deep neural networks}, + author = {Jonathan Berrisch and Micha{\l} Narajewski and Florian Ziel}, + year = {2023}, + month = {7}, + journal = {Energy and AI}, + publisher = {Elsevier BV}, + volume = {13}, + pages = {100236}, + doi = {10.1016/j.egyai.2023.100236}, + issn = {2666-5468}, + keywords = {Electricity peak load, Generalized additive models, Artificial neural networks, Prediction, Combination, Weather effects, Seasonality} } \ No newline at end of file diff --git a/custom.scss b/custom.scss index 9bfd78e..1193483 100644 --- a/custom.scss +++ b/custom.scss @@ -235,4 +235,11 @@ .fa { width: 1.25em; text-align: center; +} + +/* Grey out links inside a row with class "greyed-out" */ +tr.greyed-out, +tr.greyed-out a { + color: var(--col_grey_3) !important; + text-decoration: none !important; } \ No newline at end of file diff --git a/index.qmd b/index.qmd index 117055d..e20cd1f 100644 --- a/index.qmd +++ b/index.qmd @@ -98,11 +98,197 @@ col_orange <- "#ffa600" col_yellow <- "#FCE135" ``` +## Motivation + + +## Overview of the Thesis {transition="fade" transition-speed="slow"} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Berrisch, J., & Ziel, F. [-@BERRISCH2023105221]. CRPS learning. Journal of Econometrics, 237(2), 105221. +
+ + + Berrisch, J., & Ziel, F. [-@BERRISCH20241568]. Multivariate probabilistic CRPS learning with an application to day-ahead electricity prices. International Journal of Forecasting, 40(4), 1568–1586. +
+ + + Hirsch, S., Berrisch, J., & Ziel, F. [-@hirsch2024online]. Online Distributional Regression. arXiv preprint arXiv:2407.08750. +
+ + + Berrisch, J., & Ziel, F. [-@berrisch2022distributional]. Distributional modeling and forecasting of natural gas prices. Journal of Forecasting, 41(6), 1065–1086. +
+ + + Berrisch, J., Pappert, S., Ziel, F., & Arsova, A. [-@berrisch2023modeling]. Modeling volatility and dependence of European carbon and energy prices. Finance Research Letters, 52, 103503. +
+ + + Berrisch, J., Narajewski, M., & Ziel, F. [-@BERRISCH2023100236]. High-resolution peak demand estimation using generalized additive models and deep neural networks. Energy and AI, 13, 100236. +
+ + + Berrisch, J. [-@berrisch2025rcpptimer]. rcpptimer: Rcpp Tic-Toc Timer with OpenMP Support. arXiv preprint arXiv:2501.15856. +
+ +## Overview of the Thesis {transition="fade" transition-speed="slow"} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Berrisch, J., & Ziel, F. [-@BERRISCH2023105221]. CRPS learning. Journal of Econometrics, 237(2), 105221. +
+ + + Berrisch, J., & Ziel, F. [-@BERRISCH20241568]. Multivariate probabilistic CRPS learning with an application to day-ahead electricity prices. International Journal of Forecasting, 40(4), 1568–1586. +
+ + + Hirsch, S., Berrisch, J., & Ziel, F. [-@hirsch2024online]. Online Distributional Regression. arXiv preprint arXiv:2407.08750. +
+ + + Berrisch, J., & Ziel, F. [-@berrisch2022distributional]. Distributional modeling and forecasting of natural gas prices. Journal of Forecasting, 41(6), 1065–1086. +
+ + + Berrisch, J., Pappert, S., Ziel, F., & Arsova, A. [-@berrisch2023modeling]. Modeling volatility and dependence of European carbon and energy prices. Finance Research Letters, 52, 103503. +
+ + + Berrisch, J., Narajewski, M., & Ziel, F. [-@BERRISCH2023100236]. High-resolution peak demand estimation using generalized additive models and deep neural networks. Energy and AI, 13, 100236. +
+ + + Berrisch, J. [-@berrisch2025rcpptimer]. rcpptimer: Rcpp Tic-Toc Timer with OpenMP Support. arXiv preprint arXiv:2501.15856. +
+ +## Overview of the Thesis {transition="fade" transition-speed="slow"} + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Berrisch, J., & Ziel, F. [-@BERRISCH2023105221]. CRPS learning. Journal of Econometrics, 237(2), 105221. +
+ + + Berrisch, J., & Ziel, F. [-@BERRISCH20241568]. Multivariate probabilistic CRPS learning with an application to day-ahead electricity prices. International Journal of Forecasting, 40(4), 1568–1586. +
+ + + Hirsch, S., Berrisch, J., & Ziel, F. [-@hirsch2024online]. Online Distributional Regression. arXiv preprint arXiv:2407.08750. +
+ + + Berrisch, J., & Ziel, F. [-@berrisch2022distributional]. Distributional modeling and forecasting of natural gas prices. Journal of Forecasting, 41(6), 1065–1086. +
+ + + Berrisch, J., Pappert, S., Ziel, F., & Arsova, A. [-@berrisch2023modeling]. Modeling volatility and dependence of European carbon and energy prices. Finance Research Letters, 52, 103503. +
+ + + Berrisch, J., Narajewski, M., & Ziel, F. [-@BERRISCH2023100236]. High-resolution peak demand estimation using generalized additive models and deep neural networks. Energy and AI, 13, 100236. +
+ + + Berrisch, J. [-@berrisch2025rcpptimer]. rcpptimer: Rcpp Tic-Toc Timer with OpenMP Support. arXiv preprint arXiv:2501.15856. +
+ # CRPS Learning -Berrisch, J., & Ziel, F. (2023). *Journal of Econometrics*, 237(2), 105221. +Berrisch, J., & Ziel, F. [-@BERRISCH2023105221]. *Journal of Econometrics*, 237(2), 105221. -## Motivation +## Introduction :::: {.columns} @@ -482,7 +668,7 @@ EWA satisfies optimal selection convergence \eqref{eq_optp_select} in a determin - Loss $\ell$ is exp-concave - Learning-rate $\eta$ is chosen correctly -Those results can be converted to stochastic iid settings @kakade2008generalization, @gaillard2014second. +Those results can be converted to any stochastic setting @wintenberger2017optimal. Optimal convex aggregation convergence \eqref{eq_optp_conv} can be satisfied by applying the kernel-trick: @@ -532,8 +718,11 @@ Using the CRPS, we can calculate time-adaptive weights $w_{t,k}$. However, what ## Almost Optimal Convergence +:::: {style="font-size: 90%;"} -`r fontawesome::fa("exclamation", fill = col_orange)` QL is convex, but not exp-concave `r fontawesome::fa("arrow-right", fill ="#000000")` Bernstein Online Aggregation (BOA) lets us weaken the exp-concavity condition. It satisfies that there exist a $C>0$ such that for $x>0$ it holds that +`r fontawesome::fa("exclamation", fill = col_orange)` QL is convex, but not exp-concave + +`r fontawesome::fa("arrow-right", fill ="#000000")` Bernstein Online Aggregation (BOA) lets us weaken the exp-concavity condition. It satisfies that there exist a $C>0$ such that for $x>0$ it holds that \begin{equation} P\left( \frac{1}{t}\left(\widetilde{\mathcal{R}}_t - \widehat{\mathcal{R}}_{t,\pi} \right) \leq C \log(\log(t)) \left(\sqrt{\frac{\log(K)}{t}} + \frac{\log(K)+x}{t}\right) \right) \geq @@ -557,6 +746,8 @@ if $Y_t$ is bounded, the considered loss $\ell$ is convex $G$-Lipschitz and weak `r fontawesome::fa("arrow-right", fill ="#000000")` We show that this holds for QL under feasible conditions. +::: + ## Conditions + Lemma @@ -564,34 +755,6 @@ if $Y_t$ is bounded, the considered loss $\ell$ is convex $G$-Lipschitz and weak ::: {.column width="48%"} -**A1** - -For some $G>0$ it holds -for all $x_1,x_2\in \mathbb{R}$ and $t>0$ that - -$$ | \ell(x_1, Y_t)-\ell(x_2, Y_t) | \leq G |x_1-x_2|$$ - -**A2** For some $\alpha>0$, $\beta\in[0,1]$ it holds -for all $x_1,x_2 \in \mathbb{R}$ and $t>0$ that - -\begin{align*} - \mathbb{E}[ - & \ell(x_1, Y_t)-\ell(x_2, Y_t) | \mathcal{F}_{t-1}] \leq \\ - & \mathbb{E}[ \ell'(x_1, Y_t)(x_1 - x_2) |\mathcal{F}_{t-1}] \\ - & + - \mathbb{E}\left[ \left. \left( \alpha(\ell'(x_1, Y_t)(x_1 - x_2))^{2}\right)^{1/\beta} \right|\mathcal{F}_{t-1}\right] -\end{align*} - -`r fontawesome::fa("arrow-right", fill ="#000000")` Almost optimal w.r.t. *selection* \eqref{eq_optp_select} @gaillard2018efficient. - -::: - -::: {.column width="2%"} - -::: - -::: {.column width="48%"} - **Lemma 1** \begin{align} @@ -617,6 +780,34 @@ QL is Lipschitz continuous: ::: +::: {.column width="2%"} + +::: + +::: {.column width="48%"} + +**A1** + +For some $G>0$ it holds +for all $x_1,x_2\in \mathbb{R}$ and $t>0$ that + +$$ | \ell(x_1, Y_t)-\ell(x_2, Y_t) | \leq G |x_1-x_2|$$ + +**A2** For some $\alpha>0$, $\beta\in[0,1]$ it holds +for all $x_1,x_2 \in \mathbb{R}$ and $t>0$ that + +\begin{align*} + \mathbb{E}[ + & \ell(x_1, Y_t)-\ell(x_2, Y_t) | \mathcal{F}_{t-1}] \leq \\ + & \mathbb{E}[ \ell'(x_1, Y_t)(x_1 - x_2) |\mathcal{F}_{t-1}] \\ + & + + \mathbb{E}\left[ \left. \left( \alpha(\ell'(x_1, Y_t)(x_1 - x_2))^{2}\right)^{1/\beta} \right|\mathcal{F}_{t-1}\right] +\end{align*} + +`r fontawesome::fa("arrow-right", fill ="#000000")` Almost optimal w.r.t. *selection* \eqref{eq_optp_select} @gaillard2018efficient. + +::: + :::: ## Proposition + Theorem @@ -639,7 +830,7 @@ $\mathcal{Q}_p'' = f.$ Additionally, if $f$ is a continuous Lebesgue-density with $f\geq\gamma>0$ for some constant $\gamma>0$ on its support $\text{spt}(f)$ then is $\mathcal{Q}_p$ is $\gamma$-strongly convex. -Strong convexity with $\beta=1$ implies **A2** `r fontawesome::fa("check", fill ="#ffa600")` @gaillard2018efficient +Strong convexity with $\beta=1$ implies weak exp-concavity **A2** `r fontawesome::fa("check", fill ="#ffa600")` @gaillard2018efficient ::: @@ -1092,30 +1283,6 @@ weights_preprocessed %>% :::: -## Possible Extensions - - -**Forgetting** - -- Only taking part of the old cumulative regret into account -- Exponential forgetting of past regret - -\begin{align*} - R_{t,k} & = R_{t-1,k}(1-\xi) + \ell(\widetilde{F}_{t},Y_i) - \ell(\widehat{F}_{t,k},Y_i) \label{eq_regret_forget} -\end{align*} - -**Fixed Shares** @herbster1998tracking - - - Adding fixed shares to the weights - - Shrinkage towards a constant solution - -\begin{align*} - \widetilde{w}_{t,k} = \rho \frac{1}{K} + (1-\rho) w_{t,k} - \label{fixed_share_simple}. -\end{align*} - -TODO: Move these to the multivariate slides - ## Application Study ::: {.panel-tabset} diff --git a/remove_unused_bibentries.sh b/remove_unused_bibentries.sh new file mode 100755 index 0000000..4b92202 --- /dev/null +++ b/remove_unused_bibentries.sh @@ -0,0 +1,53 @@ +#!/bin/sh + +# Path to your .bib file +BIBFILE="assets/library.bib" + +# Step 1: Extract all citation keys from the .bib file +# This assumes keys follow immediately after @...{KEY, +bibkeys=$(grep -oP '^@\w+\{\K[^,]+' "$BIBFILE") + +# Step 2: Initialize array for unused keys +unused=() + +for key in $bibkeys; do + if ! grep -qr "$key" . --include="*.qmd"; then + unused+=("$key") + fi +done + +if [ ${#unused[@]} -eq 0 ]; then + echo "All citation keys are used." +else + echo "Unused citation keys:" + for key in "${unused[@]}"; do + echo " $key" + done +fi + +# Step 5: Optional removal +if [ ${#unused[@]} -gt 0 ]; then + echo "Removing unused entries from $BIBFILE..." + + awk -v keys="${unused[*]}" ' + BEGIN { + split(keys, klist); + for (i in klist) unused[klist[i]] = 1; + skip = 0; + } + /^[[:space:]]*@/ { + match($0, /^@\w+\{([^,]+),/, m); + if (m[1] && unused[m[1]]) { + skip = 1; + next; + } + } + skip && /^\s*}/ { + skip = 0; + next; + } + !skip + ' "$BIBFILE" > "$BIBFILE.tmp" && mv "$BIBFILE.tmp" "$BIBFILE" + + echo "Done." +fi \ No newline at end of file