| Type: | Package |
| Title: | Wavelet Based LSTM 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: | A wavelet-based LSTM model is a type of neural network architecture that uses wavelet technique to pre-process the input data before passing it through a Long Short-Term Memory (LSTM) network. The wavelet-based LSTM model is a powerful approach that combines the benefits of wavelet analysis and LSTM networks to improve the accuracy of predictions in various applications. This package has been developed using the algorithm of Anjoy and Paul (2017) and Paul and Garai (2021) <doi:10.1007/s00521-017-3289-9> <doi:10.1007/s00500-021-06087-4>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | caret, dplyr, caretForecast, tseries, stats, wavelets, TSLSTM |
| RoxygenNote: | 7.2.1 |
| NeedsCompilation: | no |
| Packaged: | 2023-04-05 18:31:57 UTC; YEASIN |
| Repository: | CRAN |
| Date/Publication: | 2023-04-06 10:03:25 UTC |
Wavelet Based LSTM Model
Description
Wavelet Based LSTM Model
Usage
WaveletLSTM(
ts,
MLag = 12,
split_ratio = 0.8,
wlevels = 3,
epochs = 25,
LSTM_unit = 20
)
Arguments
ts |
Time Series Data |
MLag |
Maximum Lags |
split_ratio |
Training and Testing Split |
wlevels |
Wavelet Levels |
epochs |
Number of epochs |
LSTM_unit |
Number of unit in LSTM layer |
Value
Train_actual: Actual train series
Test_actual: Actual test series
Train_fitted: Fitted train series
Test_predicted: Predicted test series
References
Paul, R.K. and Garai, S. (2021). Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices, Soft Computing, 25(20), 12857-12873
Examples
y<-rnorm(100,mean=100,sd=50)
WTSLSTM<-WaveletLSTM(ts=y)