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Activity Number: 574 - Recent Advances in Software
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #304489
Title: Analytical Likelihood Derivatives for State Space Forecasting Models
Author(s): Jonathan Hosking* and Ramesh Natarajan
Companies: and
Keywords: time series; maximum likelihood; computation

State space models are a flexible and widely used family of statistical models for time series analysis and forecasting. Calibration of the models to historical data is greatly facilitated by the availability of analytical derivatives of the log-likelihood function. We ave obtained a new expression for these derivatives in terms of quantities routinely computed in Kalman filtering and smoothing. This result makes it straightforward to construct an optimization method based on gradient descent using analytical log-likelihood derivatives. We present the derivation and give some examples of the gain in speed of calibration when analytical derivatives are used.

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

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