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Activity Number:
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12
- High-Dimensional Parameter Learning on Spatio-Temporal Hidden Markov Models and Its Applications in Epidemiology
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Type:
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Invited
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Date/Time:
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Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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IMS
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Abstract #316905
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Title:
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Partially Observed Time Series Models Using Long Short-Term Memory Models
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Author(s):
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Yves Atchade*
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Companies:
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Boston University
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Keywords:
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Times Series;
Partially observed time series;
LSTM
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Abstract:
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Long short-term memory (LSTM) models are commonly used in machine learning to deal with time series data. The use of these models with partially observed time series data has not been widely explored. This talk describes a minimum distance estimation procedure for fitting LSTM models with partially observed time series data.
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Authors who are presenting talks have a * after their name.
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