| Type: | Package |
| Title: | Wavelet Based K-Nearest Neighbor Model |
| Version: | 0.1.0 |
| Author: | Dr. Ranjit Kumar Paul [aut], Dr. Md Yeasin [aut, cre] |
| Maintainer: | Dr. Md Yeasin <yeasin.iasri@gmail.com> |
| Description: | The employment of the Wavelet decomposition technique proves to be highly advantageous in the modelling of noisy time series data. Wavelet decomposition technique using the "haar" algorithm has been incorporated to formulate a hybrid Wavelet KNN (K-Nearest Neighbour) model for time series forecasting, as proposed by Anjoy and Paul (2017) <doi:10.1007/s00521-017-3289-9>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | caret, dplyr, caretForecast, Metrics, tseries, stats, wavelets |
| RoxygenNote: | 7.2.1 |
| NeedsCompilation: | no |
| Packaged: | 2023-04-05 11:00:49 UTC; YEASIN |
| Repository: | CRAN |
| Date/Publication: | 2023-04-05 18:23:19 UTC |
Wavelet Based K-Nearest Neighbor Model
Description
Wavelet Based K-Nearest Neighbor Model
Usage
WaveletKNN(ts, MLag = 12, split_ratio = 0.8, wlevels = 3)
Arguments
ts |
Time Series Data |
MLag |
Maximum Lags |
split_ratio |
Training and Testing Split |
wlevels |
Number of Wavelet Levels |
Value
Lag: Lags used in model
Parameters: Parameters of the model
Train_actual: Actual train series
Test_actual: Actual test series
Train_fitted: Fitted train series
Test_predicted: Predicted test series
Accuracy: RMSE and MAPE of the model
References
Aminghafari, M. and Poggi, J.M. 2012. Nonstationary time series forecasting using wavelets and kernel smoothing. Communications in Statistics-Theory and Methods, 41(3),485-499.
Paul, R.K. A and Anjoy, P. 2018. Modeling fractionally integrated maximum temperature series in India in presence of structural break. Theory and Applied Climatology 134, 241–249.
Examples
library("WaveletKNN")
data<- rnorm(100,100, 10)
WG<-WaveletKNN(ts=data)