lforecast function

Compute long term forecasts.

Performs multi-step forecasts by iteratively using 1-ahead predictions as inputs

lforecast(M, data, start, horizon)

Arguments

• M: fitted model, the object returned by fit.
• data: training data, typically built using CasesSeries.
• start: starting period (when out-of-samples start).
• horizon: number of multi-step predictions.

Details

Check the reference for details.

Returns

Returns a numeric vector with the multi-step predictions.

References

• This tutorial shows additional code examples:

  P. Cortez.

  A tutorial on using the rminer R package for data mining tasks.

  Teaching Report, Department of Information Systems, ALGORITMI Research Centre, Engineering School, University of Minho, Guimaraes, Portugal, July 2015.

  http://hdl.handle.net/1822/36210

• To check for more details:

  P. Cortez.

  Sensitivity Analysis for Time Lag Selection to Forecast Seasonal Time Series using Neural Networks and Support Vector Machines.

  In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2010), pp. 3694-3701, Barcelona, Spain, July, 2010. IEEE Computer Society, ISBN: 978-1-4244-6917-8 (DVD edition).

  tools:::Rd_expr_doi("10.1109/IJCNN.2010.5596890")

Author(s)

Paulo Cortez http://www3.dsi.uminho.pt/pcortez/

See Also

fit, CasesSeries, predict.fit, mgraph.

Examples

ts=c(1,4,7,2,5,8,3,6,9,4,7,10,5,8,11,6,9)
d=CasesSeries(ts,c(1,2,3))
M=fit(y~.,d[1:7,],model="mlpe",search=2)
P1=predict(M,d[8:14,]) # single-step predictions
P2=lforecast(M,d,8,7) # multi-step predictions, horizon=7
print(mmetric(d$y[8:14],P1,"MAE"))
print(mmetric(d$y[8:14],P2,"MAE"))
L=vector("list",2); pred=vector("list",1);test=vector("list",1)
pred[[1]]=P1; test[[1]]=d$y[8:14]; L[[1]]=list(pred=pred,test=test,runs=1)
pred[[1]]=P2; test[[1]]=d$y[8:14]; L[[2]]=list(pred=pred,test=test,runs=1)
mgraph(L,graph="REG",Grid=10,leg=c("y","P1","P2"),col=c("black","cyan","blue"))
mgraph(L,graph="RSC",Grid=10,leg=c("P1","P2"),col=c("cyan","blue"))