Hello,
If you want to predict crop yields based on (selected) factors
, then you are doing classical predictive modelling on cross-sectional data.
It's NOT forecasting ... Forecasting is a sub-field of predictive modelling where you work on time series data.
( If you have time-series data for several cross-sections then you are dealing with panel data )
Because you are thinking about using NN , I suppose you are working on observational data that were collected in the past in the context of normal agricultural activities.
If the data still has to be collected, it's better to set up an experiment, like here :
SAS/QC 15.2 User's Guide
The OPTEX Procedure
Example 15.9 Optimal Design in the Presence of Covariance
(this is about comparing the effects of seven different fertilizers on crop yield)
https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/qcug/qcug_optex_examples09.htm
But OK, let's assume you already have your data in a data set.
For predictive modelling, basic NN architectures will do.
Try multilayer perceptron first.
See also here :
https://www.sas.com/en_be/industry/agriculture-analytics.html
And one more thing :
due to the fact lots of weights (parameters) have to be estimated in NN, you need a lot of data (a lot of rows) to get acceptable confidence limits around your parameter estimates.
If you do not have a lot of data (let alone having room for data splitting)
, it might be better to turn to classical statistical techniques like mixed modeling or regression (without random effects).
Kind regards,
Koen
Hello,
The term "Artificial neural network" refers to a biologically inspired sub-field of artificial intelligence modeled after the brain.
All the neural networks you use in SAS (and other software) are "artificial".
NN is a vast field.
Good luck,
Koen
Hello,
If you want to predict crop yields based on (selected) factors
, then you are doing classical predictive modelling on cross-sectional data.
It's NOT forecasting ... Forecasting is a sub-field of predictive modelling where you work on time series data.
( If you have time-series data for several cross-sections then you are dealing with panel data )
Because you are thinking about using NN , I suppose you are working on observational data that were collected in the past in the context of normal agricultural activities.
If the data still has to be collected, it's better to set up an experiment, like here :
SAS/QC 15.2 User's Guide
The OPTEX Procedure
Example 15.9 Optimal Design in the Presence of Covariance
(this is about comparing the effects of seven different fertilizers on crop yield)
https://go.documentation.sas.com/doc/en/pgmsascdc/9.4_3.5/qcug/qcug_optex_examples09.htm
But OK, let's assume you already have your data in a data set.
For predictive modelling, basic NN architectures will do.
Try multilayer perceptron first.
See also here :
https://www.sas.com/en_be/industry/agriculture-analytics.html
And one more thing :
due to the fact lots of weights (parameters) have to be estimated in NN, you need a lot of data (a lot of rows) to get acceptable confidence limits around your parameter estimates.
If you do not have a lot of data (let alone having room for data splitting)
, it might be better to turn to classical statistical techniques like mixed modeling or regression (without random effects).
Kind regards,
Koen
Something extra ...
Neural networks demystified
By Leo Sadovy on SAS Voices March 23, 2016
https://blogs.sas.com/content/sascom/2016/03/23/neural-networks-demystified/
Koen
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 25. Read more here about why you should contribute and what is in it for you!
ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.
Find more tutorials on the SAS Users YouTube channel.