Building Better Forecasting Models With Transfer Functions ( 2019-EU-30MP-089 )
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Jian Cao, Principal Systems Engineer, JMP Division, SAS
Best Invited Paper Finalist
How to model and forecast the time series when it is interrupted due to interventions (e.g., process changes)? If you have leading indicators or other exogenous variables how can you incorporate them into your ARIMA models to make better forecast?
In this paper I will try to demystify the transfer function models in JMP with key use cases: Regression with ARIMA Errors, Distributed Lag Models and Intervention Models. I will demonstrate the benefits of using the transfer functions over the Ordinary Least Squares regression and ARIMA for building better forecasting models.