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Oral
Learning Theory and Algorithms for Forecasting Non-stationary Time Series
Vitaly Kuznetsov · Mehryar Mohri

Wed Dec 09 06:50 AM -- 07:10 AM (PST) @ Room 210 A
in Oral Session 5 »

We present data-dependent learning bounds for the general scenario of non-stationary non-mixing stochastic processes. Our learning guarantees are expressed in terms of a data-dependent measure of sequential complexity and a discrepancy measure that can be estimated from data under some mild assumptions. We use our learning bounds to devise new algorithms for non-stationary time series forecasting for which we report some preliminary experimental results.

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Author Information

Vitaly Kuznetsov (Google Research)

Vitaly Kuznetsov is a research scientist at Google. Prior to joining Google Research, Vitaly received his Ph.D. in mathematics from the Courant Institute of Mathematical Sciences at New York University. Vitaly has contributed to a number of different areas in machine learning, in particular the development of the theory and algorithms for forecasting non-stationary time series. At Google, his work is focused on the design and implementation of large-scale machine learning tools and algorithms for time series modeling, forecasting and anomaly detection. His current research interests include all aspects of applied and theoretical time series analysis, in particular, in non-stationary environments.

Mehryar Mohri (Courant Institute and Google)

Mehryar Mohri is a Professor of Computer Science and Mathematics at the Courant Institute of Mathematical Sciences and a Research Consultant at Google. Prior to these positions, he spent about ten years at AT&T Bell Labs, later AT&T Labs-Research, where he served for several years as a Department Head and a Technology Leader. His research interests cover a number of different areas: primarily machine learning, algorithms and theory, automata theory, speech processing, natural language processing, and also computational biology. His research in learning theory and algorithms has been used in a variety of applications. His work on automata theory and algorithms has served as the foundation for several applications in language processing, with several of his algorithms used in virtually all spoken-dialog and speech recognitions systems used in the United States. He has co-authored several software libraries widely used in research and academic labs. He is also co-author of the machine learning textbook Foundations of Machine Learning used in graduate courses on machine learning in several universities and corporate research laboratories.

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