Package 'WaveletSVR'

Title: Wavelet-SVR Hybrid Model for Time Series Forecasting
Description: The main aim of this package is to combine the advantage of wavelet and support vector machine models for time series forecasting. This package also gives the accuracy measurements in terms of RMSE and MAPE. This package fits the hybrid Wavelet SVR model for time series forecasting The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Raimundo and Okamoto (2018) <DOI: 10.1109/INFOCT.2018.8356851>.
Authors: Ranjit Kumar Paul [aut, cre], Md Yeasin [aut]
Maintainer: Ranjit Kumar Paul <[email protected]>
License: GPL-3
Version: 0.1.0
Built: 2025-02-14 04:42:47 UTC
Source: https://github.com/cran/WaveletSVR

Help Index

Wavelet Transform Using Maximal Overlap Discrete Wavelet Transform (MODWT) Algorithm

Description

Transforms the time series data by using hybrid MODWT algorithm.

Usage

WaveletFitting(
  ts,
  Wvlevels,
  WFilter = "haar",
  bndry = "periodic",
  FFlag = TRUE
)

Arguments

ts

Univariate time series
Wvlevels

The level of wavelet decomposition
WFilter

Wavelet filter use in the decomposition
bndry

The boundary condition of wavelet decomposition:'periodic' or 'reflection'
FFlag

The FastFlag condition of wavelet decomposition: True or False

Value

  • WaveletSeries - The wavelet trasnform of the series

References

  • Aminghafari, M. and Poggi, J.M. 2007. Forecasting time series using wavelets. Internationa Journal of Wavelets, Multiresolution and Inforamtion Processing, 5, 709 to 724

  • Percival D. B. and Walden A. T. 2000. Wavelet Methods for Time-Series Analysis. Cambridge Univ. Press, U.K.

  • Paul R. K., Prajneshu and Ghosh H. 2013. Wavelet Frequency Domain Approach for Modelling and Forecasting of Indian Monsoon Rainfall Time-Series Data. Journal of the Indian society of agricultural statistics, 67, 319 to 327.

Examples

data<-rnorm(100,mean=100,sd=50)
Wavelet<-WaveletFitting(ts=data,Wvlevels=3,WFilter='haar',bndry='periodic',FFlag=TRUE)

Wavelet-SVR Hybrid Model for Forecasting

Description

The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE).

Usage

WaveletFittingsvr(
  ts,
  tlag = ACF,
  Waveletlevels,
  WaveletFilter = "haar",
  boundary = "periodic",
  FastFlag = TRUE,
  SplitRatio = 0.8
)

Arguments

ts

Univariate time series
tlag

Number of lags
Waveletlevels

The level of wavelet decomposition
WaveletFilter

Wavelet filter use in the decomposition
boundary

The boundary condition of wavelet decomposition
FastFlag

The FastFlag condition of wavelet decomposition: True or False
SplitRatio

Training and testing data split

Value

  • TrainFittedValue - Fitted value of train data

  • TestPredictedValue - Predicted value of test data

  • AccuracyTable - RMSE and MAPE of train and test data

References

  • Aminghafari, M. and Poggi, J.M. 2007. Forecasting time series using wavelets. Internationa Journal of Wavelets, Multiresolution and Inforamtion Processing, 5, 709 to 724

  • Percival, D. B. and Walden A. T. 2000. Wavelet Methods for Time-Series Analysis. Cambridge Univ. Press, U.K.

  • Paul, R. K., Prajneshu and Ghosh H. 2013. Wavelet Frequency Domain Approach for Modelling and Forecasting of Indian Monsoon Rainfall Time-Series Data. Journal of the Indian society of agricultural statistics, 67, 319 to 327.

  • Paul, R. K. and Birthal, P.S. 2015. Investigating rainfall trend over India using wavelet technique. Journal of Water and Climate Change, 7, 365 to 378.

  • Paul, R. K. 2015. ARIMAX-GARCH-WAVELET Model for forecasting volatile data. Model Assisted Statistics and Application, 10, 243 to 252.

  • Raimundo, M. S. and Okamoto, J. 2018. SVR-wavelet adaptive model for forecasting financial time series. International Conference on Information and Computer Technologies (ICICT), 111-114, doi: 10.1109/INFOCT.2018.8356851.

Examples

data<-rnorm(100,mean=100,sd=50)
WSVR<-WaveletFittingsvr(ts=data,tlag=2,Waveletlevels=3)