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Activity Number: 350 - New Methods for Time Series and Longitudinal Data
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #304352
Title: Recurrent Neural Networks for ARMA Model Selection
Author(s): Bei Chen* and Beat Buesser and Kelsey DiPietro
Companies: IBM Research and IBM Research and University of Notre Dame
Keywords: RNN; Time Series; Model Selection; Networks; DL; Forecasting
Abstract:

Selecting an appropriate ARMA model for a given time series is a classic problem in statistics that is encountered in many applications. Typically, this involves a human-in-the-loop and repeated parameter evaluation of candidate models, which is not ideal for learning at scale. We propose a Long Short Term Memory (LSTM) classification model for automatic ARMA model selection. Our numerical experiments show that the proposed method is fast and provides better accuracy than the traditional Box-Jenkins approach based on autocorrelations and model selection criteria. We demonstrate the application of our approach with a case study on volatility prediction of daily stock prices.


Authors who are presenting talks have a * after their name.

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