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DATA SCIENCE
MACHINE LEARNING
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About |
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Temporal data are frequently encountered in a wide range of domains such as bio-informatics, medicine, finance and engineering, among many others. They are naturally present in applications covering language, motion and vision analysis, or more emerging ones as energy efficient building, smart cities, dynamic social media or sensor networks. Contrary to static data, temporal data are of complex nature, they are generally noisy, of high dimensionality, they may be non stationary (i.e. first order statistics vary with time) and irregular (involving several time granularities), they may have several invariant domain-dependent factors as time delay, translation, scale or tendency effects. These temporal peculiarities make limited the majority of standard statistical models and machine learning approaches, that mainly assume i.i.d data, homoscedasticity, normality of residuals, etc. To tackle such challenging temporal data, one appeals for new advanced approaches at the intersection of statistics, time series analysis, signal processing and machine learning. Defining new approaches that transcend boundaries between several domains to extract valuable information from temporal data is undeniably a hot topic in the near future, that has been yet the subject of active research this last decade. |
Call for Papers |
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The proposed workshop welcomes papers that cover, but are not limited to, one or several of the following topics:
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Summary |
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AALTD 2020 : Workshop on Advanced Analytics and Learning on Temporal Data will take place in Ghent, Belgium. It’s a 1 day event starting on Sep 18, 2020 (Friday) and will be winded up on Sep 18, 2020 (Friday). AALTD 2020 falls under the following areas: DATA SCIENCE, MACHINE LEARNING, etc. Submissions for this Workshop can be made by Jun 09, 2020. Authors can expect the result of submission by Jul 09, 2020. Upon acceptance, authors should submit the final version of the manuscript on or before Jul 28, 2020 to the official website of the Workshop. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the AALTD 2020
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Credits and Sources |
| [1] AALTD 2020 : Workshop on Advanced Analytics and Learning on Temporal Data |
