Description: This package conducts periodicity analysis of a single
time series and coherence analysis of two time series using
wavelet method. Confidence interval is calculated using

Author: Angi Roesch and Harald Schmidbauer
Description: Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms.

Author: Angi Roesch <angi@angi-stat.com> and Harald Schmidbauer <harald@hs-stat.com>

Title: Fit the Wavelet-GARCH Model to Volatile Time Series Data
Author: Dr. Ranjit Kumar Paul, Sandipan Samanta and Ankit Tanwar
Description: Fits the combination of Wavelet-GARCH model for time series forecasting using algorithm by Paul (2015) <doi:10.3233/MAS-150328>.
Imports: stats, wavelets, FinTS, forecast, parallel, rugarch, fracdiff
Packaged: 2019-10-15 09:56:16 UTC; ranjitstat

Imports: stats, wavelets, FinTS, forecast, parallel, rugarch, fracdiff,
Packaged: 2020-02-29 10:51:18 UTC; ranjitstat

Title: Wavelet-RF Hybrid Model for Time Series Forecasting
Authors@R: c(person("Ranjit Kumar", "Paul", role = c("aut","cre"), email = "ranjitstat@gmail.com"),
Description: The Wavelet Decomposition followed by Random Forest Regression (RF) models have been applied for time series forecasting. The maximum overlap discrete wavelet transform (MODWT) algorithm was chosen as it works for any length of the series. The series is first divided into training and testing sets. In each of the wavelet decomposed series, the  supervised machine learning approach namely random forest was employed to train the model. This package also provides accuracy metrics in the form of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Ding et al. (2021) <DOI: 10.1007/s11356-020-12298-3>.
Imports: stats, wavelets, fracdiff, forecast, randomForest, tsutils

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>.
Imports: stats, wavelets, fracdiff, forecast, e1071, tsutils