my_svr: Specially Designed SVR-Based Modeling

In AriGaMyANNSVR: Hybrid ARIMA-GARCH and Two Specially Designed ML-Based Models

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 documentation built on April 14, 2023, 12:36 a.m.