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SUMMARY;CHARSET=UTF-8:Summer Academy Session: Time Series Analytics
URL:https://www.sfg.at/e/summer-academy-session-time-series-analytics/
DESCRIPTION;CHARSET=UTF-8:Time Series Analytics\nDriven primarily by the on
 going digitization and the Industry 4.0 revolution\, time series data has 
 become ubiquitous and of high imprtance in terms of relevance.\n\nTime Ser
 ies Analytics\n\n19.08.2020 15:00 &#8211\; 17:00 | Language: English\n\n\n
 Target group:\nResearchers\, engineers and innovation managers from manufa
 cturing industries (e.g. automotive\, semiconductors\, machinery…). Repr
 esentatives from energy\, health and financial domains might also be inter
 ested.\nAbstract:\nDriven primarily by the ongoing digitization and the In
 dustry 4.0 revolution\, time series data has become ubiquitous. Innumerabl
 e sensors installed at various stages of an industrial production process 
 are generating data at a high rate. Modern IoT (Internet of Things) archit
 ectures support the transfer of collected data over high-bandwidth network
 s\, and provide means for high-performance processing on the edge and in t
 he cloud. The capability to collect massive amounts of time series data\, 
 paired with modern data analytics and machine learning methods\, opens the
  way to novel applications with the potential of greatly benefiting the ef
 ficiency and the resiliency of industrial production processes. Besides ma
 nufacturing machinery\, time series data is created en masse in numerous o
 ther situations such as human diagnostics in medicine\, energy production 
 and consumption\, agriculture\, financial transaction tracking\, personal 
 mobile devices (smartphones)\, and many others. Clearly\, methods for time
  series analytics have a far broader area of application than manufacturin
 g industry alone.\nIn this session\, we will present selected research con
 tributions addressing analytics of time series data. In particular\, you w
 ill hear about distance measures and seasonality detection\, event detecti
 on and prediction using deep learning methods\, and methods for finding pa
 tterns in large time series databases. To illustrate the benefits arising 
 from the application of these techniques\, we will present results from se
 lected past or running projects. Examples include modality detection in ap
 plication domains such as mobility or precision agriculture\, event classi
 fication for predictive maintenance\, or gathering expert knowledge to imp
 rove algorithm performance.\nFinally\, we wish to learn about your needs a
 nd how we could address those needs in an effective manner. What problems 
 are you expecting to solve with time series analytics in your organization
 ? Did you already gather experience in automating data-oriented tasks with
  AI algorithms? Which problems (e.g. data availability/security\, legal is
 sues\, employee acceptance) might prevent you to introduce data analytics 
 in your work processes? Time for discussion will be allocated after each t
 alk. Your input and your feedback will be highly appreciated!\nAfter the e
 vent you will know:\nAbout bleeding-edge methods for analyzing time series
  data\, such as\n\n\n\ndetection and prediction in time series data\n\n\n\
 n\n\n\ndeep learning on time series\n\n\n\n\n\n\nfeature extraction from t
 ime series\n\n\n\n\n\n\nsearching in time-series information\n\n\n\nYou wi
 ll also learn about success stories and applications\, such as\n\n\n\npred
 ictive maintenance in manufacturing\n\n\n\n\n\n\ninteractive applications 
 for time series analytics\n\n\n\n\nSpeaker\n\n\n\n\n\nVedran Sabol\nResear
 ch Area Manager Knowledge Visualization\n\n\n\n\n\n\n\nEduardo Veas\nResea
 rch Area Manager Knowledge Visualization\n\n\n\n\n\n\n\nRoman Kern\nResear
 ch Area Manager Knowledge Discovery\n\n\n\n\n\n\n\nLucas Iacono\nSenior Sc
 ientist Area Knowledge Visualization\n\n\n\n\n\n\n\nMaximilian Toller\nPhD
  Researcher Area Knowledge Discovery\n\n\n\n\n
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