Indian Machinery and Transport Equipment Exports - Forecasting with External Factors Using Chain of Hybrid Sarimax-Garch Model

Abstract

To choose the best forecasting model, it is essential to comprehend time series data since external influences like social, economic, and political events may affect the way the data behave. This study considers outside variables that could have an impact on the target variable used in improving the predictions. India Machinery and Transport Equipment Dataset is gathered from various sources, are cleaned, pre-processed, the missing values are removed, data types are converted, and dependent variables are identified before being used. By incorporating the SARIMAX model with the GARCH model and experimenting with various parameters and conditions, the current study seeks to enhance it. The SARIMAX-GARCH Model is a time series forecasting method used to predict market swings and export values. A helper model is developed to forecast the exogenous value to forecast the export value, which is then used as input for the final model. The ideal parameters for boosting the hybrid model's performance were identified through hyperparameter tuning. The results of this study provide estimates for future export values and contribute to a better understanding of India's Machinery and Transport Equipment export market. This research work focuses on export value forecasting with the use of future exogenous variables. Exogenous factors are essential for predicting market changes and, as a result, support the forecasting of precise export values.