Deep Learning for Multivariate Time-series Analysis
Deep Learning for Multivariate Time-series Analysis


Time-series analysis has been a major challenge in many real-world applications, e.g. finance, speech, biomedical or industrial sensors, where data is captured as a sequence over time. In most of these applications, predictive models have to be built to perform classification, regression or forecasting. Traditional techniques usually depend on engineering new features that can abstract away the time-dependencies which then can be used to train a machine learning model. Feature engineering usually requires strong domain knowledge and is often computationally expensive to run during model deployment. Recently, recurrent neural networks (RNNs) have been making great progress in becoming the best theoretical model for multivariate time-series analysis. With enough training, RNNs are able to capture time-dependencies in the data and find their relationship to the model output. Additionally, convolutional neural networks (CNNs), with their success in image processing, were recently shown to perform really well in time-series classification and regression tasks, creating very useful feature map representation of the data. In this talk, I will demonstrate how RNNs and CNNs are used in time-series analysis and how combining them together can solve many different time-series applications that were difficult to solve before the advent of deep learning.


Sari Andoni is a Senior Data Scientist at SparkCognition, Inc. He has extensive experience in machine learning, neural networks and deep learning combined with a research background in neurobiology. With published research in leading journals, Sari currently focuses on automated model building with multivariate time-series data using artificial neural networks. He received his Bachelors degree in Computer Sciences and Mathematics from Brigham Young University, and a PhD from the Institute for Neuroscience at The University of Texas at Austin. For his dissertation, Sari studied the auditory midbrain and how the auditory system classifies natural vocalizations into behaviorally relevant perceptions. In his postdoctoral research, he studied the visual system focusing on the interaction of spontaneous activity with stimulus-evoked responses in the thalamocortical circuit.

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