Department of Data Science and Analytics

Publications

• Moss, Jonas & Grønneberg, Steffen (2023)
  Partial Identification of Latent Correlations with Ordinal Data
  Psychometrika, 88, p. 241-252. Doi: 10.1007/s11336-022-09898-y
• Moss, Jonas (2023)
  Measuring Agreement Using Guessing Models and Knowledge Coefficients
  Psychometrika. Doi: 10.1007/s11336-023-09919-4
• Høst, Anders Mølmen; Lison, Pierre & Moonen, Leon (2023)
  Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Database
• Miroshnychenko, Ivan; Vocalelli, Giorgio, De Massis, Alfredo, Grassi, Stefano & Ravazzolo, Francesco (2023)
  The COVID-19 pandemic and family business performance
  Small Business Economics. Doi: 10.1007/s11187-023-00766-2
• Bashiri Behmiri, Niaz; Fezzi, Carlo & Ravazzolo, Francesco (2023)
  Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks
  Energy, 278. Doi: 10.1016/j.energy.2023.127831
• Casarin, Roberto; Grassi, Stefano, Ravazzolo, Francesco & van Dijk, Herman K. (2023)
  A flexible predictive density combination for large financial data sets in regular and crisis periods
  Journal of Econometrics. Doi: 10.1016/j.jeconom.2022.11.004
• Foroni, Claudia; Ravazzolo, Francesco & Rossini, Luca (2023)
  Are low frequency macroeconomic variables important for high frequency electricity prices?
  Economic Modelling, 120. Doi: 10.1016/j.econmod.2022.106160
• Langguth, Johannes; Schroeder, Daniel Thilo, Filkukova, Petra, Brenner, Stefan, Phillips, Jesper & Pogorelov, Konstantin (2023)
  COCO: an annotated Twitter dataset of COVID-19 conspiracy theories
  Journal of Computational Social Science (JCSS). Doi: 10.1007/s42001-023-00200-3
• Iwaszkiewicz-Eggebrecht, Elzbieta; Ronquist, Fredrik, Łukasik, Piotr, Granqvist, Emma, Buczek, Mateusz, Prus, Monika, Kudlicka, Jan, Roslin, Tomas, Tack, Ayco J. M., Andersson, Anders F. & Miraldo, Andreia (2023)
  Optimizing insect metabarcoding using replicated mock communities
  Methods in Ecology and Evolution, 14(4), p. 1130-1146. Doi: 10.1111/2041-210X.14073
• Juelsrud, Ragnar Enger & Larsen, Vegard Høghaug (2023)
  Macroeconomic uncertainty and bank lending
  Economics Letters, 225. Doi: 10.1016/j.econlet.2023.111041
  Show summary

  We investigate the impact of macro-related uncertainty on bank lending in Norway. We show that an increase in general macroeconomic uncertainty reduces bank lending. Importantly, however, we show that this effect is largely driven by monetary policy uncertainty, suggesting that uncertainty about the monetary policy stance is key for understanding why macro-related uncertainty impacts bank lending.

• Billé, Anna Gloria; Tomelleri, Alessio & Ravazzolo, Francesco (2023)
  Forecasting regional GDPs: a comparison with spatial dynamic panel data models
  Spatial Economic Analysis. Doi: 10.1080/17421772.2023.2199034
• Galdi, Giulio; Casarin, Roberto, Ferrari, Davide, Fezzi, Carlo & Ravazzolo, Francesco (2023)
  Nowcasting industrial production using linear and non-linear models of electricity demand
  Energy Economics, 126. Doi: 10.1016/j.eneco.2023.107006
• Lee, Daesoo; Aune, Erlend & Malacarne, Sara (2023)
  Vector Quantized Time Series Generation with a Bidirectional Prior Model
  Proceedings of Machine Learning Research (PMLR), 206, p. 7665-7693.
• Fronzetti Colladon, Andrea; Grippa, Francesca, Guardabascio, Barbara, Costante, Gabriele & Ravazzolo, Francesco (2023)
  Forecasting consumer confidence through semantic network analysis of online news
  Scientific Reports, 13(1). Doi: 10.1038/s41598-023-38400-6
• Haugsdal, Espen; Aune, Erlend & Ruocco, Massimiliano (2023)
  Persistence Initialization: a novel adaptation of the Transformer architecture for time series forecasting
  Applied intelligence (Boston), p. 1-16. Doi: 10.1007/s10489-023-04927-4
• Iacopini, Matteo; Ravazzolo, Francesco & Rossini, Luca (2022)
  Proper Scoring Rules for Evaluating Density Forecasts with Asymmetric Loss Functions
  Journal of business & economic statistics. Doi: 10.1080/07350015.2022.2035229
• Avesani, Diego; Zanfei, Ariele, Di Marco, Nicola, Galletti, Andrea, Ravazzolo, Francesco, Righetti, Maurizio & Majone, Bruno (2022)
  Short-term hydropower optimization driven by innovative time-adapting econometric model
  Applied Energy, 310. Doi: 10.1016/j.apenergy.2021.118510
• Moss, Jonas (2022)
  Infinite diameter confidence sets in Hedges’ publication bias model
  Journal of the Korean Statistical Society. Doi: 10.1007/s42952-022-00169-1
• Fronzetti Colladon, Andrea; Grassi, Stefano, Ravazzolo, Francesco & Violante, Francesco (2022)
  Forecasting financial markets with semantic network analysis in the COVID-19 crisis
  Journal of Forecasting. Doi: 10.1002/for.2936
• Stoltenberg, Emil Aas; Mykland, Per A. & Zhang, Lan (2022)
  A CLT FOR SECOND DIFFERENCE ESTIMATORS WITH AN APPLICATION TO VOLATILITY AND INTENSITY
  Annals of Statistics, 50(4), p. 2072-2095. Doi: 10.1214/22-AOS2176
• Hougen, Conrad D.; Kaplan, Lance M., Ivanovska, Magdalena, Cerutti, Federico, Mishra, Kumar Vijay & Hero III, Alfred O. (2022)
  SOLBP: Second-Order Loopy Belief Propagation for Inference in Uncertain Bayesian Networks
  Doi: 10.23919/FUSION49751.2022.9841392
• Billé, Anna Gloria; Gianfreda, Angelica, Del Grosso, Filippo & Ravazzolo, Francesco (2022)
  Forecasting electricity prices with expert, linear, and nonlinear models
  International Journal of Forecasting. Doi: 10.1016/j.ijforecast.2022.01.003
• Lundén, Daniel; Öhman, Joey, Kudlicka, Jan, Senderov, Viktor, Ronquist, Fredrik & Broman, David (2022)
  Compiling Universal Probabilistic Programming Languages with Efficient Parallel Sequential Monte Carlo Inference
  Doi: 10.1007/978-3-030-99336-8_2
• Langguth, Johannes; Filkukova, Petra, Brenner, Stefan, Schroeder, Daniel Thilo & Pogorelov, Konstantin (2022)
  COVID-19 and 5G conspiracy theories: long term observation of a digital wildfire
  International Journal of Data Science and Analytics (JDSA). Doi: 10.1007/s41060-022-00322-3
• Ivanovska, Magdalena & Slavkovik, Marija (2022)
  Probabilistic Judgement Aggregation by Opinion Update
  Doi: 10.1007/978-3-031-13448-7_3
• Langguth, Johannes; Tumanis, Aigar & Azad, Ariful (2022)
  Incremental Clustering Algorithms for Massive Dynamic Graphs
  Doi: 10.1109/ICDMW53433.2021.00051
  Show summary

  We consider the problem of incremental graph clustering where the graph to be clustered is given as a sequence of disjoint subsets of the edge set. The problem appears when dealing with graphs that are created over time, such as online social networks where new users appear continuously, or protein interaction networks when new proteins are discovered. For very large graphs, it is computationally too expensive to repeatedly apply standard clustering algorithms. Instead, algorithms whose time complexity only depends on the size of the incoming subset of edges in every step are needed. At the same time, such algorithms should find clusterings whose quality is close to that produced by offline algorithms. In this paper, we discuss the computational model and present an incremental clustering algorithm. We test the algorithm performance and quality on a wide variety of instances. Our results show that the algorithm far outperforms offline algorithms while retaining a large fraction of their clustering quality.

• Huber, Andreas; Schröder, Daniel Thilo, Pogorelov, Konstantin, Griwodz, Carsten & Langguth, Johannes (2022)
  A Streaming System for Large-scale Temporal Graph Mining of Reddit Data
  Doi: 10.1109/IPDPSW55747.2022.00189
• Andrade Mancisidor, Rogelio; Kampffmeyer, Michael, Aas, Kjersti & Jenssen, Robert (2022)
  Generating customer's credit behavior with deep generative models
  Knowledge-Based Systems, 245. Doi: 10.1016/j.knosys.2022.108568
• Straume, Hans-Martin; Asche, Frank, Oglend, Atle, Abrahamsen, Eirik Bjorheim, Birkenbach, Anna M., Langguth, Johannes, Lanquepin, Guillaume & Roll, Kristin Helen (2022)
  Impacts of Covid-19 on Norwegian salmon exports: A firm-level analysis
  Aquaculture, 561. Doi: 10.1016/j.aquaculture.2022.738678
• Yang, Wei-Ting; Reis, Marco, Borodin, Valeria, Juge, Michel & Roussy, Agnès (2022)
  An interpretable unsupervised Bayesian network model for fault detection and diagnosis
  Control Engineering Practice, 127. Doi: 10.1016/j.conengprac.2022.105304
• Gianfreda, Angelica; Ravazzolo, Francesco & Rossini, Luca (2022)
  Large Time-Varying Volatility Models for Hourly Electricity Prices*
  Oxford Bulletin of Economics and Statistics. Doi: 10.1111/obes.12532
• Durante, Fabrizio; Gianfreda, Angelica, Ravazzolo, Francesco & Rossini, Luca (2022)
  A multivariate dependence analysis for electricity prices, demand and renewable energy sources
  Information Sciences, 590, p. 74-89. Doi: 10.1016/j.ins.2022.01.003
• Agudze, Komla M.; Billio, Monica, Casarin, Roberto & Ravazzolo, Francesco (2021)
  Markov switching panel with endogenous synchronization effects
  Journal of Econometrics, p. 1-18. Doi: 10.1016/j.jeconom.2021.04.004
• Hjort, Nils Lid & Stoltenberg, Emil Aas (2021)
  The partly parametric and partly nonparametric additive risk model
  Lifetime Data Analysis. Doi: 10.1007/s10985-021-09535-3
• Yazidi, Anis; Ivanovska, Magdalena, Zennaro, Fabio Massimo, Lind, Pedro & Viedma, Enrique Herrera (2021)
  A new decision making model based on Rank Centrality for GDM with fuzzy preference relations
  European Journal of Operational Research, 297, p. 1030-1041. Doi: 10.1016/j.ejor.2021.05.030
• Burchard, Luk; Cai, Xing & Langguth, Johannes (2021)
  iPUG for Multiple Graphcore IPUs: Optimizing Performance and Scalability of Parallel Breadth-First Search
  Doi: 10.1109/HiPC53243.2021.00030
• Burchard, Luk; Moe, Johannes Sellæg, Schroeder, Daniel Thilo, Pogorelov, Konstantin & Langguth, Johannes (2021)
  iPUG: Accelerating Breadth-First Graph Traversals Using Manycore Graphcore IPUs
  Doi: 10.1007/978-3-030-78713-4_16
• Ferrari, Davide; Ravazzolo, Francesco & Vespignani, Joaquin (2021)
  Forecasting energy commodity prices: A large global dataset sparse approach
  Energy Economics, 98. Doi: 10.1016/j.eneco.2021.105268
• Caporin, Massimiliano; Gupta, Rangan & Ravazzolo, Francesco (2021)
  Contagion between real estate and financial markets: A Bayesian quantile-on-quantile approach
  The North American journal of economics and finance, 55, p. 1-12. Doi: 10.1016/j.najef.2020.101347
• Ravazzolo, Francesco & Vespignani, Joaquin (2020)
  World steel production: A new monthly indicator of global real economic activity
  Canadian Journal of Economics. Doi: 10.1111/caje.12442
• Vassøy, Bjørnar; Ruocco, Massimiliano, de Souza da Silva, Eliezer & Aune, Erlend (2019)
  Time is of the essence: A joint Hierarchical RNN and Point Process model for time and item predictions
  Doi: 10.1145/3289600.3290987
• Yang, Wei-Ting; Blue, Jakey, Roussy, Agnès, Reis, Marco & Pinaton, Jacques (2018)
  Virtual metrology modeling based on gaussian bayesian network
  Winter simulation conference : proceedings. Doi: 10.1109/WSC.2018.8632485
• Bassetti, Federico; Casarin, Roberto & Ravazzolo, Francesco (2018)
  Bayesian Nonparametric Calibration and Combination of Predictive Distributions
  Journal of the American Statistical Association. Doi: 10.1080/01621459.2016.1273117
• Clark, Todd E. & Ravazzolo, Francesco (2015)
  Macroeconomic Forecasting Performance under Alternative Specifications of Time-Varying Volatility
  Journal of applied econometrics, 30(4), p. 551-575. Doi: 10.1002/jae.2379