Forecasting Automobile Retail Sales Using Data Mining: The Case of Ranchi, Jharkhand, India - Mahto, Gyaneshwar; Prasad, Umesh; Dwivedi, Rajiv Kumar (International Journal of Computer Sciences and Engineering, 2018)
In this article, sales forecast models for the automobile market are developed and tested. Enhanced sales forecast methodologies and models for the automobile market are presented. The methods used deliver highly accurate predictions while maintaining the ability to explain the underlying model at the same time. The representation of the economic training data is discussed, as well as its effects on the newly registered automobiles to be predicted. Our most important criteria for the assessment of these models are the quality of the prediction as well as an easy explicability.The automobile market are presented for the evaluation of the forecast models. The market demand for vehicles has increased in recent years everywhere in the world. We need suitable models to understand and forecast demand of vehicle. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The vehicles sales in beautiful city Ranchi, Capital of Jharkhand, India, are predicted in both short term (up to December 2018) and long term (up to 2021), as proofs of the growth of the Motor Vehicles industry.