Jan Gasthaus

Publications by Year

Research Areas

Time Series Analysis and Forecasting, Forecasting Techniques and Applications, Stock Market Forecasting Methods, Anomaly Detection Techniques and Applications, Gaussian Processes and Bayesian Inference

Most-Cited Works

• Deep State Space Models for Time Series Forecasting(2018)
• → Deep Learning for Time Series Forecasting: Tutorial and Literature Survey(2022)280 cited
• → DeepAR: Probabilistic forecasting with autoregressive recurrent networks(2019)238 cited
• → Criteria for classifying forecasting methods(2019)197 cited
• → Probabilistic demand forecasting at scale(2017)121 cited
• GluonTS: Probabilistic and Neural Time Series Modeling in Python(2020)
• → Elastic Machine Learning Algorithms in Amazon SageMaker(2020)101 cited
• → A stochastic memoizer for sequence data(2009)98 cited
• → Forecasting with trees(2021)89 cited
• Probabilistic Forecasting with Spline Quantile Function RNNs(2019)