The availability of large amounts of time series data, paired with the performance of deep learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. Vitaly Kuznetsov and Zelda Mariet compare sequence-to-sequence modeling to classical time series models and provide the first theoretical analysis of a framework that uses sequence-to-sequence models for time series forecasting.
Vitaly Kuznetsov is a research scientist at Google, where he focuses on the design and implementation of machine learning tools and algorithms for time series modeling, forecasting, and anomaly detection for a variety of practical applications ranging from supply forecasting for search ads to demand estimation in networks. Vitaly has contributed to a number of different areas in machine learning, including structured prediction, ensemble learning, deep learning, and the development of the theory and algorithms for forecasting nonstationary time series. He holds a PhD in mathematics from the Courant Institute of Mathematical Sciences at New York University.
Zelda Mariet is a fifth-year PhD student year in the Computer Science and Electrical Engineering Department at MIT, where she studies the theory and application of negatively dependent measures for machine learning model design and optimization. She has interned at Google (Brain, Research, and Machine Intelligence), where she studied problems related to time series prediction and determinantal point processes. Zelda is a recipient of the 2018 Google PhD Fellowship in machine learning. She holds a BS and MS from École Polytechnique in France.
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