into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) <arXiv:1910.13152>.

into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) <doi:10.1007/s13253-020-00407-1>.
Suggests: knitr, rmarkdown, ggplot2, sampling, BalancedSampling, stats

Description: Fits hybrid Wavelet ANN model for time series forecasting using algorithm by Anjoy and Paul (2017) <DOI: 10.1007/s00521-017-3289-9>.
Imports: stats, wavelets, fracdiff, forecast

Authors@R: c(person(" Dr. Ranjit Kumar", "Paul", role = c("aut","cre"), email = "ranjitstat@gmail.com"),
person("Dr. Md", "Yeasin", role = "aut", email = "yeasin.iasri@gmail.com"))
Description: The wavelet and ANN technique have been combined to reduce the effect of data noise. This wavelet-ANN conjunction model is able to forecast time series data with better accuracy than the traditional time series model. This package fits hybrid Wavelet ANN model for time series forecasting using algorithm by Anjoy and Paul (2017) <DOI: 10.1007/s00521-017-3289-9>.
Imports: stats, wavelets, fracdiff, forecast, Metrics

Author: Ranjit Kumar Paul and Sandipan Samanta
Maintainer: Ranjit Kumar Paul <ranjitstat@gmail.com>
Description: Fits hybrid Wavelet ARIMA model for time series forecasting using algorithm by Aminghafari and Poggi (2012) <doi:10.1142/S0219691307002002>.
Packaged: 2017-10-25 04:50:16 UTC; ranjitstat

Packaged: 2018-06-01 11:30:56 UTC; ranjitstat

Title: Wavelet-ARIMA Model for Time Series Forecasting
person("Mr. Sandipan", "Samanta", role = "aut", email = "sondiponsamanta@gmail.com"),
Description: Noise in the time-series data significantly affects the accuracy of the ARIMA model. Wavelet transformation decomposes the time series data into subcomponents to reduce the noise and help to improve the model performance. The wavelet-ARIMA model can achieve higher prediction accuracy than the traditional ARIMA model. This package provides Wavelet-ARIMA model for time series forecasting based on the algorithm by Aminghafari and Poggi (2012) and Paul and Anjoy (2018) <doi:10.1142/S0219691307002002> <doi:10.1007/s00704-017-2271-x>.