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pro vyhledávání: '"Turowski, Marian"'
Autor:
Heidrich, Benedikt, Phipps, Kaleb, Neumann, Oliver, Turowski, Marian, Mikut, Ralf, Hagenmeyer, Veit
Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic forecasts are performed on time series data that contain calendar-dri
Externí odkaz:
http://arxiv.org/abs/2302.02597
Autor:
Phipps, Kaleb, Heidrich, Benedikt, Turowski, Marian, Wittig, Moritz, Mikut, Ralf, Hagenmeyer, Veit
In various applications, probabilistic forecasts are required to quantify the inherent uncertainty associated with the forecast. However, numerous modern forecasting methods are still designed to create deterministic forecasts. Transforming these det
Externí odkaz:
http://arxiv.org/abs/2302.01800
ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles
Autor:
Quintana, Matias, Stoeckmann, Till, Park, June Young, Turowski, Marian, Hagenmeyer, Veit, Miller, Clayton
Publikováno v:
Energy & Buildings. 2022;265: 112096
Data-driven building energy prediction is an integral part of the process for measurement and verification, building benchmarking, and building-to-grid interaction. The ASHRAE Great Energy Predictor III (GEPIII) machine learning competition used an e
Externí odkaz:
http://arxiv.org/abs/2203.06618
Autor:
Meisenbacher, Stefan, Turowski, Marian, Phipps, Kaleb, Rätz, Martin, Müller, Dirk, Hagenmeyer, Veit, Mikut, Ralf
Publikováno v:
WIREs Data Mining and Knowledge Discovery (2022) e1475
Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process
Externí odkaz:
http://arxiv.org/abs/2202.01712
Autor:
Neumann, Oliver, Ludwig, Nicole, Turowski, Marian, Heidrich, Benedikt, Hagenmeyer, Veit, Mikut, Ralf
Publikováno v:
Schulte, H. Proceedings - 31. Workshop Computational Intelligence : Berlin, 25. - 26. November 2021. KIT Scientific Publishing, 2021
Deep Neural Networks are able to solve many complex tasks with less engineering effort and better performance. However, these networks often use data for training and evaluation without investigating its representation, i.e.~the form of the used data
Externí odkaz:
http://arxiv.org/abs/2111.09128
Autor:
Heidrich, Benedikt, Bartschat, Andreas, Turowski, Marian, Neumann, Oliver, Phipps, Kaleb, Meisenbacher, Stefan, Schmieder, Kai, Ludwig, Nicole, Mikut, Ralf, Hagenmeyer, Veit
Time series data are fundamental for a variety of applications, ranging from financial markets to energy systems. Due to their importance, the number and complexity of tools and methods used for time series analysis is constantly increasing. However,
Externí odkaz:
http://arxiv.org/abs/2106.10157
Autor:
Weber, Moritz, Turowski, Marian, Çakmak, Hüseyin K., Mikut, Ralf, Kühnapfel, Uwe, Hagenmeyer, Veit
A cornerstone of the worldwide transition to smart grids are smart meters. Smart meters typically collect and provide energy time series that are vital for various applications, such as grid simulations, fault-detection, load forecasting, load analys
Externí odkaz:
http://arxiv.org/abs/2101.01423
Akademický článek
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Publikováno v:
Energy Informatics; 11/2/2023, Vol. 6 Issue 1, p1-23, 23p
Autor:
Heidrich, Benedikt, Phipps, Kaleb, Meisenbacher, Stefan, Turowski, Marian, Neumann, Oliver, Mikut, Ralf, Hagenmeyer, Veit
pyWATTS is an open-source Python-based workflow automation tool for time series analysis. pyWATTS simplifies the evaluation process and the design of repetitive machine learning experiments with time series by providing a powerful pipeline solution i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1dd749a819ce51d06efd075ed9def03