Marian Turowski

Orcid: 0000-0002-3776-2215

According to our database¹, Marian Turowski authored at least 20 papers between 2014 and 2024.

Collaborative distances:

Dijkstra number² of five.
Erdős number³ of four.

Timeline

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Bibliography

2024

Generating probabilistic forecasts from arbitrary point forecasts using a conditional invertible neural network.

[BibT_eX]

[DOI]

Appl. Intell., April, 2024

2023

Controlling non-stationarity and periodicities in time series generation using conditional invertible neural networks.

[BibT_eX]

[DOI]

Appl. Intell., April, 2023

Using weather data in energy time series forecasting: the benefit of input data transformations.

[BibT_eX]

[DOI]

Energy Inform., January, 2023

Data-Driven Methods for Managing Anomalies in Energy Time Series.

[BibT_eX]

[DOI]

Marian Turowski

PhD thesis, 2023

ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information.

[BibT_eX]

[DOI]

CoRR, 2023

Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2023

Managing Anomalies in Energy Time Series for Automated Forecasting.

[BibT_eX]

[DOI]

Proceedings of the Energy Informatics - Third Energy Informatics Academy Conference, 2023

Loss-Customised Probabilistic Energy Time Series Forecasts Using Automated Hyperparameter Optimisation.

[BibT_eX]

[DOI]

Proceedings of the 14th ACM International Conference on Future Energy Systems, 2023

2022

Review of automated time series forecasting pipelines.

[BibT_eX]

[DOI]

WIREs Data Mining Knowl. Discov., 2022

Boost short-term load forecasts with synthetic data from transferred latent space information.

[BibT_eX]

[DOI]

Energy Inform., 2022

ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles.

[BibT_eX]

[DOI]

CoRR, 2022

Modeling and generating synthetic anomalies for energy and power time series.

[BibT_eX]

[DOI]

Proceedings of the e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022, 2022

Enhancing anomaly detection methods for energy time series using latent space data representations.

[BibT_eX]

[DOI]

Proceedings of the e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022, 2022

Adaptively coping with concept drifts in energy time series forecasting using profiles.

[BibT_eX]

[DOI]

Proceedings of the e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022, 2022

2021

Data-Driven Copy-Paste Imputation for Energy Time Series.

[BibT_eX]

[DOI]

IEEE Trans. Smart Grid, 2021

Smart Data Representations: Impact on the Accuracy of Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2021

pyWATTS: Python Workflow Automation Tool for Time Series.

[BibT_eX]

[DOI]

CoRR, 2021

2020

Point and contextual anomaly detection in building load profiles of a university campus.

[BibT_eX]

[DOI]

Proceedings of the IEEE PES Innovative Smart Grid Technologies Europe, 2020

Forecasting energy time series with profile neural networks.

[BibT_eX]

[DOI]

Proceedings of the e-Energy '20: The Eleventh ACM International Conference on Future Energy Systems, 2020

2014

Vertical Scaling Capability of OpenStack - Survey of Guest Operating Systems, Hypervisors, and the Cloud Management Platform.

[BibT_eX]

[DOI]

Marian Turowski

Alexander Lenk

Proceedings of the Service-Oriented Computing - ICSOC 2014 Workshops, 2014

Marian Turowski

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