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WaveST
View on CRAN: Click
here
Download and install WaveST package within the R console
Install from CRAN:
install.packages("WaveST")
Install from Github:
library("remotes")
install_github("cran/WaveST") Install by package version:
library("remotes")
install_version("WaveST", "0.1.0") Attach the package and use:
library("WaveST")
Maintained by
Dr. Ranjit Kumar Paul
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-03-16
Latest Update: 2026-03-16
Description:
An integrated wavelet-based spatial time series modelling framework designed to enhance predictive accuracy under noisy and nonstationary conditions by jointly exploiting multi-resolution (wavelet) information and spatial dependence. The package implements WaveSARIMA() (Wavelet Based Spatial AutoRegressive Integrated Moving Average model using regression features with forecast::auto.arima()) and WaveSNN() (Wavelet Based Spatial Neural Network model using neuralnet with hyperparameter search). Both functions support spatial transformation via a user-supplied spatial matrix, lag feature construction, MODWT-based wavelet sub-series feature generation, time-ordered train/test splitting, and performance evaluation (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-squared (R²), and Mean Absolute Percentage Error (MAPE)), returning fitted models and actual vs predicted values for train and test sets. The package has been developed using the algorithm of Paul et al. (2023) <doi:10.1007/s43538-025-00581-1>.
How to cite:
Dr. Ranjit Kumar Paul (2026). WaveST: Wavelet-Based Spatial Time Series Models. R package version 0.1.0, https://cran.r-project.org/web/packages/WaveST. Accessed 13 Oct. 2026.
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