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Moss, Jonas & Grønneberg, Steffen
(2023)
Partial Identification of Latent Correlations with Ordinal Data
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Moss, Jonas
(2023)
Measuring Agreement Using Guessing Models and Knowledge Coefficients
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Høst, Anders Mølmen; Lison, Pierre & Moonen, Leon
(2023)
Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Database
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Miroshnychenko, Ivan; Vocalelli, Giorgio, De Massis, Alfredo, Grassi, Stefano & Ravazzolo, Francesco
(2023)
The COVID-19 pandemic and family business performance
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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
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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
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Foroni, Claudia; Ravazzolo, Francesco & Rossini, Luca
(2023)
Are low frequency macroeconomic variables important for high frequency electricity prices?
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Langguth, Johannes; Schroeder, Daniel Thilo, Filkukova, Petra, Brenner, Stefan, Phillips, Jesper & Pogorelov, Konstantin
(2023)
COCO: an annotated Twitter dataset of COVID-19 conspiracy theories
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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
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Juelsrud, Ragnar Enger & Larsen, Vegard Høghaug
(2023)
Macroeconomic uncertainty and bank lending
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.
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Billé, Anna Gloria; Tomelleri, Alessio & Ravazzolo, Francesco
(2023)
Forecasting regional GDPs: a comparison with spatial dynamic panel data models
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Galdi, Giulio; Casarin, Roberto, Ferrari, Davide, Fezzi, Carlo & Ravazzolo, Francesco
(2023)
Nowcasting industrial production using linear and non-linear models of electricity demand
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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.
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Fronzetti Colladon, Andrea; Grippa, Francesca, Guardabascio, Barbara, Costante, Gabriele & Ravazzolo, Francesco
(2023)
Forecasting consumer confidence through semantic network analysis of online news
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Haugsdal, Espen; Aune, Erlend & Ruocco, Massimiliano
(2023)
Persistence Initialization: a novel adaptation of the Transformer architecture for time series forecasting
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Iacopini, Matteo; Ravazzolo, Francesco & Rossini, Luca
(2022)
Proper Scoring Rules for Evaluating Density Forecasts with Asymmetric Loss Functions
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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
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Moss, Jonas
(2022)
Infinite diameter confidence sets in Hedges’ publication bias model
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Fronzetti Colladon, Andrea; Grassi, Stefano, Ravazzolo, Francesco & Violante, Francesco
(2022)
Forecasting financial markets with semantic network analysis in the COVID-19 crisis
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Stoltenberg, Emil Aas; Mykland, Per A. & Zhang, Lan
(2022)
A CLT FOR SECOND DIFFERENCE ESTIMATORS WITH AN APPLICATION TO VOLATILITY AND INTENSITY
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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
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Billé, Anna Gloria; Gianfreda, Angelica, Del Grosso, Filippo & Ravazzolo, Francesco
(2022)
Forecasting electricity prices with expert, linear, and nonlinear models
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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
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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
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Ivanovska, Magdalena & Slavkovik, Marija
(2022)
Probabilistic Judgement Aggregation by Opinion Update
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Langguth, Johannes; Tumanis, Aigar & Azad, Ariful
(2022)
Incremental Clustering Algorithms for Massive Dynamic Graphs
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.
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Huber, Andreas; Schröder, Daniel Thilo, Pogorelov, Konstantin, Griwodz, Carsten & Langguth, Johannes
(2022)
A Streaming System for Large-scale Temporal Graph Mining of Reddit Data
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Andrade Mancisidor, Rogelio; Kampffmeyer, Michael, Aas, Kjersti & Jenssen, Robert
(2022)
Generating customer's credit behavior with deep generative models
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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
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Yang, Wei-Ting; Reis, Marco, Borodin, Valeria, Juge, Michel & Roussy, Agnès
(2022)
An interpretable unsupervised Bayesian network model for fault detection and diagnosis
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Gianfreda, Angelica; Ravazzolo, Francesco & Rossini, Luca
(2022)
Large Time-Varying Volatility Models for Hourly Electricity Prices*
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Durante, Fabrizio; Gianfreda, Angelica, Ravazzolo, Francesco & Rossini, Luca
(2022)
A multivariate dependence analysis for electricity prices, demand and renewable energy sources
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Agudze, Komla M.; Billio, Monica, Casarin, Roberto & Ravazzolo, Francesco
(2021)
Markov switching panel with endogenous synchronization effects
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Hjort, Nils Lid & Stoltenberg, Emil Aas
(2021)
The partly parametric and partly nonparametric additive risk model
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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
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Burchard, Luk; Cai, Xing & Langguth, Johannes
(2021)
iPUG for Multiple Graphcore IPUs: Optimizing Performance and Scalability of Parallel Breadth-First Search
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Burchard, Luk; Moe, Johannes Sellæg, Schroeder, Daniel Thilo, Pogorelov, Konstantin & Langguth, Johannes
(2021)
iPUG: Accelerating Breadth-First Graph Traversals Using Manycore Graphcore IPUs
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Ferrari, Davide; Ravazzolo, Francesco & Vespignani, Joaquin
(2021)
Forecasting energy commodity prices: A large global dataset sparse approach
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Caporin, Massimiliano; Gupta, Rangan & Ravazzolo, Francesco
(2021)
Contagion between real estate and financial markets: A Bayesian quantile-on-quantile approach
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Ravazzolo, Francesco & Vespignani, Joaquin
(2020)
World steel production: A new monthly indicator of global real economic activity
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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
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Yang, Wei-Ting; Blue, Jakey, Roussy, Agnès, Reis, Marco & Pinaton, Jacques
(2018)
Virtual metrology modeling based on gaussian bayesian network
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Bassetti, Federico; Casarin, Roberto & Ravazzolo, Francesco
(2018)
Bayesian Nonparametric Calibration and Combination of Predictive Distributions
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Clark, Todd E. & Ravazzolo, Francesco
(2015)
Macroeconomic Forecasting Performance under Alternative Specifications of Time-Varying Volatility