Identifying Demand Forecasting using Machine Learning for Business Intelligence

Abstract

Making precise and valid sales prediction plays a vital role in any business organization. Modern methods that are used for sales prediction are often based on the historical income of a product. Further in these models, the corresponding timelines, adjustment of timelines, obtaining the comparative behavior of the product aids them for efficient demand forecasting. Since the product segmentation section on the E-trade platform consists of large numbers of related products, where the sales expert may meet, and attempts to include these series chain records into an integrated model. In this proposed model, on demand and off-demand relationship that is available on all products from the managers are considered. In addition to the forecast framework, a pre-scientific framework is also proposed to overcome the challenges of the E-trading business organizations. Comparing the predictive framework in the real-time global market is also achieved. Our approach accomplishes efficient outcomes when compared with the existing models.