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Activity Number: 576 - Advanced Methodological Contributions in Time Series and Forecasting
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #329333 Presentation
Title: Analytical Likelihood Derivatives for State Space Forecasting Models
Author(s): Jonathan R. M. Hosking* and Ramesh Natarajan
Companies: Amazon and Amazon
Keywords: ARIMA; optimization; time series

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 have obtained a new expression for these derivatives in terms of quantities routinely computed in Kalman filtering and smoothing. 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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