my_svr {AriGaMyANNSVR} | R Documentation |

## Specially Designed SVR-Based Modeling

### Description

Fits a specially designed SVR model to the uni-variate time series data. The contribution is related to the PhD work of the maintainer.

### Usage

```
my_svr(Y, ratio = 0.9, n_lag = 4)
```

### Arguments

`Y` |
Univariate time series |

`ratio` |
Ratio of number of observations in training and testing sets |

`n_lag` |
Lag of the provided time series data |

### Value

Output_svr: List of three data frames containing three data frames namely predict_compare, forecast_compare, and metrics

### References

Paul, R. K., & Garai, S. (2021). Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices. Soft Computing, 25(20), 12857-12873.

Paul, R. K., & Garai, S. (2022). Wavelets based artificial neural network technique for forecasting agricultural prices. Journal of the Indian Society for Probability and Statistics, 23(1), 47-61.

Garai, S., Paul, R. K., Rakshit, D., Yeasin, M., Paul, A. K., Roy, H. S., Barman, S. & Manjunatha, B. (2023). An MRA Based MLR Model for Forecasting Indian svrual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.

### Examples

```
Y <- rnorm(100, 100, 10)
result <- my_svr(Y, ratio = 0.8, n_lag = 4)
```

*AriGaMyANNSVR*version 0.1.0 Index]