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Keynote | Technical

State of the art time-series analysis with deep learning

Friday 17th | 14:00 - 14:30 | Theatre 20


Keywords defining the session:

- Machine learning

- Time-series

- Deep nets

Description:

Time series related problems have traditionally been solved using engineered features obtained by heuristic processes. Based on the recent success of recurrent neural networks for time series domains, we present a generic deep learning framework for time series based on convolutional and LSTM recurrent units, which: (i) works on raw temporal data; (ii) does not require expert knowledge in designing features; and (iii) explicitly models the temporal dynamics of features. We detail the architecture and present an actual implementation of such framework. We illustrate the performance of the framework on a use case: the problem of human activity recognition.