Vertical Scaling Capability of OpenStack - Survey of Guest Operating Systems, Hypervisors, and the Cloud Management Platform. - Marian Turowski; Alexander Lenk (ICSOC Workshops, 2014)

Forecasting energy time series with profile neural networks. - Benedikt Heidrich; Marian Turowski; Nicole Ludwig 002; Ralf Mikut; Veit Hagenmeyer (e-Energy, 2020)

Point and contextual anomaly detection in building load profiles of a university campus. - Long Wang 010; Marian Turowski; Meng Zhang; Till Riedel; Michael Beigl; Ralf Mikut; Veit Hagenmeyer (ISGT-Europe, 2020)

pyWATTS: Python Workflow Automation Tool for Time Series. - Benedikt Heidrich; Andreas Bartschat; Marian Turowski; Oliver Neumann; Kaleb Phipps; Stefan Meisenbacher; Kai Schmieder; Nicole Ludwig 002; Ralf Mikut; Veit Hagenmeyer (CoRR, 2021)

Smart Data Representations: Impact on the Accuracy of Deep Neural Networks. - Oliver Neumann; Nicole Ludwig 002; Marian Turowski; Benedikt Heidrich; Veit Hagenmeyer; Ralf Mikut (CoRR, 2021)

Data-Driven Copy-Paste Imputation for Energy Time Series. - Moritz Weber 001; Marian Turowski; Hüseyin Kemâl Çakmak; Ralf Mikut; Uwe G. Kühnapfel; Veit Hagenmeyer (IEEE Trans. Smart Grid, 2021)

Adaptively coping with concept drifts in energy time series forecasting using profiles. - Benedikt Heidrich; Nicole Ludwig 002; Marian Turowski; Ralf Mikut; Veit Hagenmeyer (e-Energy, 2022)

Enhancing anomaly detection methods for energy time series using latent space data representations. - Marian Turowski; Benedikt Heidrich; Kaleb Phipps; Kai Schmieder; Oliver Neumann; Ralf Mikut; Veit Hagenmeyer (e-Energy, 2022)

Modeling and generating synthetic anomalies for energy and power time series. - Marian Turowski; Moritz Weber 001; Oliver Neumann; Benedikt Heidrich; Kaleb Phipps; Hüseyin Kemâl Çakmak; Ralf Mikut; Veit Hagenmeyer (e-Energy, 2022)

ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles. - Matias Quintana; Till Stoeckmann; June Young Park; Marian Turowski; Veit Hagenmeyer; Clayton Miller (CoRR, 2022)

Boost short-term load forecasts with synthetic data from transferred latent space information. - Benedikt Heidrich; Lisa Mannsperger; Marian Turowski; Kaleb Phipps; Benjamin Schäfer 001; Ralf Mikut; Veit Hagenmeyer (Energy Inform., 2022)

Review of automated time series forecasting pipelines. - Stefan Meisenbacher; Marian Turowski; Kaleb Phipps; Martin Rätz; Dirk Müller 005; Veit Hagenmeyer; Ralf Mikut (WIREs Data Mining Knowl. Discov., 2022)

Loss-Customised Probabilistic Energy Time Series Forecasts Using Automated Hyperparameter Optimisation. - Kaleb Phipps; Stefan Meisenbacher; Benedikt Heidrich; Marian Turowski; Ralf Mikut; Veit Hagenmeyer (e-Energy, 2023)

Managing Anomalies in Energy Time Series for Automated Forecasting. - Marian Turowski; Oliver Neumann; Lisa Mannsperger; Kristof Kraus; Kira Layer; Ralf Mikut; Veit Hagenmeyer (EI.A, 2023)

Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks. - Kaleb Phipps; Benedikt Heidrich; Marian Turowski; Moritz Wittig; Ralf Mikut; Veit Hagenmeyer (CoRR, 2023)

ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information. - Benedikt Heidrich; Kaleb Phipps; Oliver Neumann; Marian Turowski; Ralf Mikut; Veit Hagenmeyer (CoRR, 2023)

Data-Driven Methods for Managing Anomalies in Energy Time Series. - Marian Turowski (2023)

Using weather data in energy time series forecasting: the benefit of input data transformations. - Oliver Neumann; Marian Turowski; Ralf Mikut; Veit Hagenmeyer; Nicole Ludwig 002 (Energy Inform., 2023)

Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks. - Benedikt Heidrich; Marian Turowski; Kaleb Phipps; Kai Schmieder; Wolfgang Süß; Ralf Mikut; Veit Hagenmeyer (Appl. Intell., 2023)

Generating probabilistic forecasts from arbitrary point forecasts using a conditional invertible neural network. - Kaleb Phipps; Benedikt Heidrich; Marian Turowski; Moritz Wittig; Ralf Mikut; Veit Hagenmeyer (Appl. Intell., 2024)