| Type: | Package |
| Title: | Time Series Artificial Neural Network |
| Version: | 0.1.0 |
| Author: | Md Yeasin [aut, cre], Ranjit Kumar Paul [aut], Dipro Sinha [aut] |
| Maintainer: | Md Yeasin <yeasin.iasri@gmail.com> |
| Description: | The best ANN structure for time series data analysis is a demanding need in the present era. This package will find the best-fitted ANN model based on forecasting accuracy. The optimum size of the hidden layers was also determined after determining the number of lags to be included. This package has been developed using the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.2 |
| Imports: | forecast, gtools, stats, utils |
| NeedsCompilation: | no |
| Packaged: | 2021-12-13 05:23:14 UTC; yeasi |
| Repository: | CRAN |
| Date/Publication: | 2021-12-14 08:40:07 UTC |
Time Series Artificial Neural Network
Description
The best ANN structure for time series data analysis is a demanding need in the present era. This package will find the best-fitted ANN model based on forecasting accuracy. The optimum size of the hidden layers was also determined after determining the number of lags to be included. This package has been developed using the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
Usage
Auto.TSANN(data, min.size, max.size, split.ratio)
Arguments
data |
Time Series Data |
min.size |
Minimum Size of Hidden Layer |
max.size |
Maximum Size of Hidden Layer |
split.ratio |
Training and Testing Split Ratio |
Value
A list containing:
FinalModel: Best ANN model
Trace: Matrix of All Iteration
FittedValue: Model Fitted Value
PredictedValue: Model Forecast Value of Test Data
Train.RMSE: Root Mean Square Error of Train Data
Test.RMSE: Root Mean Square Error of Test Data
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
set.seed(16)
x<-rnorm(n = 50, mean = 150, sd = 10)
Auto.TSANN(x,1,2,0.80)