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jiancao

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Jul 7, 2014

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Building Better Forecasting Models With Transfer Functions ( 2019-EU-30MP-089 )

Level: Advanced
Job Function: Analyst / Scientist / Engineer


Jian Cao, Principal Systems Engineer, JMP Division, SAS

 

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.  

Comments
yves_gueniffey

Very nice piece of work ! Thanks for all the teachers who try to teach Forecasting "Multivariate" Models with JMP as well as with SAS ETS.

frankderuyck

Nice presentation! I now try to apply this in my project on modelling parking occupation level; with Neural Net I achieve a nice R² o 0,96 however the residuals are not random and can be modeled with a seasonal ARMA (4,0,5) (0,0,1)6 model. So I now want to build a transfer function for parking occupation with as input the Neural network model and ARMA reesidual model; I am surprised to notice that the tranfer model option is not activated? Did do something wrong? I am using a regular JMP 13.2 version.

jiancao

Thanks, Frank.

Transfer Function option has been available since JMP 7. After you launch the time series with output and Input series it can be selected from the top red triangle menu.  

 

TFM.png

frankderuyck

Strange, the tranfer function oprion is in my menu list but it is blurred and I can't activate it? 

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