ariga: ARIMA-GARCH Hybrid Modeling

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

ARIMA-GARCH Hybrid Modeling

Description

First fits the time series data by using ARIMA model. If the residuals are having "arch" effect, then GARCH is fitted. Based on the previously mentioned condition final prediction is obtained.

Usage

ariga(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_ariga: 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 Annual Rainfall Using Large Scale Climate Indices. International Journal of Environment and Climate Change, 13(5), 137-150.

Examples

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

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