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

In rminer: Data Mining Classification and Regression Methods

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.

Details

Check reference for details.

Value

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

Author(s)

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

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).
  \Sexpr[results=rd]{tools:::Rd_expr_doi("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

t=1:20
d=CasesSeries(1:10,c(1,3,4))
print(d)
d=CasesSeries(1:10,c(1,2,3))
print(d)

rminer documentation built on Oct. 29, 2024, 9:06 a.m.