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SDIL,Machine Learning for Time Series Analysis

Big Data & AI Maturity, Technology Consultancy and Training
English, German

Specialized machine learning architectures are needed for applications such as remaining useful life prediction or anomaly detection in time series. We offer standardized frameworks for both feature generation and specialized deep ML models such as embedded convolutional LSTMs.


Our experts have extensive experience in analyzing time series data from various applications in industry, finance, healthcare, consumer electronics, and environment using specialized machine learning architectures. We have several frameworks and tools to help you with various analysis and modeling tasks. An advanced example is "Automatic Remaining Useful Life Estimation Framework with Embedded Convolutional LSTM as the Backbone" (doi:10.1007/978-3-030-67667-4_28). Tasks range from classification and prediction to anomaly detection and clustering. Our algorithms and systems can handle Big Data based on both volume and velocity.

However, we also support more classical analysis tasks such as feature extraction (e.g. with tsfresh) or generation as well as time series cluster analysis (e.g. tslearn) or automatic segmentation. We provide scalable systems for data labeling and learning on our HPC clusters.

The tasks are performed by experienced ML researchers at KIT. KIT is "The Research University in the Helmholtz Association". As one of the largest scientific institutions in Europe, Germany's only university of excellence with national large-scale research facilities combines a long university tradition with program-oriented cutting-edge research. Since KIT also focuses on innovation and technology transfer, our experts have many years of experience from applied industrial projects.


Conditions and requirements for participation in an experiment within the Open Calls:

By participating in an EUHubs4Data Open Call, you are initially only applying for funding that originally comes from the European Commission and is awarded by the coordinator exclusively in its own name under the conclusion of a sub-grant agreement. This sub-grant agreement does not establish a contract with KIT, neither through your application nor through a possible positive funding decision.
KIT will therefore - also in your own interest - conclude a separate written agreement with you at the start of the experiment (based on our sample cooperation agreement: https://www.sdil.de/en/euhubs4data/sdil-model-cooperation-agreement). If you decide to propose the participation of KIT and SDIL infrastructure in your experiment, you must respect the following conditions: https://www.sdil.de/en/euhubs4data/sdil-terms. We provide this information in advance to ensure maximum transparency: please contact us if you have any questions. In the unlikely event that you are unable to conduct your experiment with our participation, we will attempt to assist you in selecting alternative services before the experiment begins.

Please note that contrary to the name "service", the above description is not a genuine commercial offer, but a listing of exclusive contributions as part of a genuine eye-to-eye collaboration.

For genuine commercial offerings related to the above topics, please feel free to contact us any time outside of the Open Calls.


Clear specification of the machine learning objective, fitting labeled data set(s)


A very specific application of timeseries anomaly detection was prototyped for KIT's own 9000 emploee campus to help facility management: https://www.sdil.de/en/projects/requirement-analysis-for-energetic-construction-measures-based-on-historical-infrastructure-data-2 , you can read more details about the technology used in the related paper: https://ieeexplore.ieee.org/document/9248792


SDIL Smart Data Innovation Lab


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