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Activity Number: 253 - Contributed Poster Presentations: Section on Statistical Computing
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #330189
Title: Estimation of Space-Time ARMAX Model
Author(s): Dongping Fang*
Companies: Zurich
Keywords: time-space; ARMAX; Kalman flter; missing values; big data

Space-time Autoregressive-moving-average model with exogenous inputs (STARMAX) model models how a time-space target process depends on its own and its neighbors' past, and on other time-space exogenous inputs. Time-space data arise in many physical science studies. The interpretability of STARMAX model makes it highly desirable. There is not a ready to use program that will fit this model. This paper extends the model by introducing the similar time-space lag structure to exogenous inputs. This paper also develops and implements a fast Kalman filter parameter estimation and prediction procedure that allows missing values in target process, and can be applied on big data.

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

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