Forecasting plays a significant role in making effective business decisions. Many products are launched in the market every year, and the initial sales of the products are very much essential for gaining insights into the sustainability of the product in the market. But because of the data limitation, forecasting with traditional models becomes a huge problem, and it is fraught with risks hence, estimates can often be off the mark. So, generating accurate forecasts becomes crucial as they can help the companies in assessing overall performance, budgeting, risk management, and cost reduction. In this paper, Forecasting of Recently Launched Products with limited data using different techniques is presented. Multiple approaches have been tested and compared to a baseline strategy to solve the above problem. For testing, the results from all the models implemented have been compared using various statistical error measures such as the Mean Absolute Percentage Error (MAPE), the Weighted Mean Absolute Percentage Error (WAPE), and Mean Absolute error (MAE).
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