rminer (version 1.4.6)

CasesSeries: Create a training set (data.frame) from a time series using a sliding window.

Description

Create a training set (data.frame) from a time series using a sliding window.

Usage

CasesSeries(t, W, start = 1, end = length(t))

Arguments

t

a time series (numeric vector).

W

a sliding window (with time lags, numeric vector).

start

starting period.

end

ending period.

Value

Returns a data.frame, where y is the output target and the inputs are the time lags.

Details

Check reference for details.

References

• 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

• 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

See Also

fit, lforecast, predict.fit.

Examples

# NOT RUN {
t=1:20
d=CasesSeries(1:10,c(1,3,4))
print(d)
d=CasesSeries(1:10,c(1,2,3))
print(d)
# }