| lforecast {rminer} | R Documentation |
Compute long term forecasts.
Description
Performs multi-step forecasts by iteratively using 1-ahead predictions as inputs
Usage
lforecast(M, data, start, horizon)
Arguments
M |
fitted model, the object returned by |
data |
training data, typically built using |
start |
starting period (when out-of-samples start). |
horizon |
number of multi-step predictions. |
Details
Check the reference for details.
Value
Returns a numeric vector with the multi-step predictions.
Author(s)
Paulo Cortez http://www3.dsi.uminho.pt/pcortez/
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).
http://dx.doi.org/10.1109/IJCNN.2010.5596890
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"))