DeepAR: Probabilistic forecasting with autoregressive recurrent networks von Salinas, David, Flunkert, Valentin, Gasthaus, Jan, Januschowski, Tim

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Criteria for classifying forecasting methods von Januschowski, Tim, Gasthaus, Jan, Wang, Yuyang, Salinas, David, Flunkert, Valentin, Bohlke-Schneider, Michael, Callot, Laurent

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Deep Learning for Time Series Forecasting: Tutorial and Literature Survey von Benidis, Konstantinos, Rangapuram, Syama Sundar, Flunkert, Valentin, Wang, Yuyang, Maddix, Danielle, Turkmen, Caner, Gasthaus, Jan, Bohlke-Schneider, Michael, Salinas, David, Stella, Lorenzo, Aubet, François-Xavier, Callot, Laurent, Januschowski, Tim

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Forecasting with trees von Januschowski, Tim, Wang, Yuyang, Torkkola, Kari, Erkkilä, Timo, Hasson, Hilaf, Gasthaus, Jan

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Forecasting big time series: old and new von Faloutsos, Christos, Gasthaus, Jan, Januschowski, Tim, Wang, Yuyang

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GluonTS: Probabilistic and Neural Time Series Modeling in Python von Alexandrov, Alexander, Benidis, Konstantinos, Bohlke-Schneider, Michael, Flunkert, Valentin, Gasthaus, Jan, Januschowski, Tim, Maddix, Danielle C., Rangapuram, Syama, Salinas, David, Schulz, Jasper, Stella, Lorenzo, Tuerkmen, Ali Caner, Wang, Yuyang

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Probabilistic demand forecasting at scale von Böse, Joos-Hendrik, Flunkert, Valentin, Gasthaus, Jan, Januschowski, Tim, Lange, Dustin, Salinas, David, Schelter, Sebastian, Seeger, Matthias, Wang, Yuyang

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DeepAR: Probabilistic forecasting with autoregressive recurrent networks (vol 36, pg 1181, 2020) von Salinas, David, Flunkert, Valentin, Gasthaus, Jan, Januschowski, Tim

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A Flexible Forecasting Stack von Januschowski, Tim, Wang, Yuyang, Gasthaus, Jan, Rangapuram, Syama, Türkmen, Caner, Zschiegner, Jasper, Stella, Lorenzo, Bohlke-Schneider, Michael, Maddix, Danielle, Benidis, Konstantinos, Alexandrov, Alexander, Faloutsos, Christos, Schelter, Sebastian

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GP-Select: Accelerating EM Using Adaptive Subspace Preselection von Shelton, Jacquelyn A, Gasthaus, Jan, Dai, Zhenwen, Lücke, Jörg, Gretton, Arthur

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Hierarchical Bayesian Nonparametric Models for Power-Law Sequences von Gasthaus, Jan Alexander

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Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models von Ayed, Fadhel, Stella, Lorenzo, Januschowski, Tim, Gasthaus, Jan

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Monte Carlo EM for Deep Time Series Anomaly Detection von Aubet, François-Xavier, Zügner, Daniel, Gasthaus, Jan

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On the detrimental effect of invariances in the likelihood for variational inference von Kurle, Richard, Herbrich, Ralf, Januschowski, Tim, Wang, Yuyang, Gasthaus, Jan

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Deep Non-Parametric Time Series Forecaster von Rangapuram, Syama Sundar, Gasthaus, Jan, Stella, Lorenzo, Flunkert, Valentin, Salinas, David, Wang, Yuyang, Januschowski, Tim

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Neural Contextual Anomaly Detection for Time Series von Carmona, Chris U, Aubet, François-Xavier, Flunkert, Valentin, Gasthaus, Jan

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Criteria for Classifying Forecasting Methods von Januschowski, Tim, Gasthaus, Jan, Wang, Yuyang, Salinas, David, Flunkert, Valentin, Bohlke-Schneider, Michael, Callot, Laurent

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Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models von Ayed, Fadhel, Stella, Lorenzo, Januschowski, Tim, Gasthaus, Jan

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Multivariate Quantile Function Forecaster von Kan, Kelvin, Aubet, François-Xavier, Januschowski, Tim, Park, Youngsuk, Benidis, Konstantinos, Ruthotto, Lars, Gasthaus, Jan

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The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models von Rabanser, Stephan, Januschowski, Tim, Flunkert, Valentin, Salinas, David, Gasthaus, Jan

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