Neural Prophet-based Weekly Sales Forecasting Model

Abstract: For companies in all sorts of different sectors, sales forecasting is an essential part of long-term strategy. Optimization of inventories, effective allocation of resources, and informed decision-making are all made possible by accurate sales predictions. For a long time, this function has been filled by conventional time series forecasting techniques like ARIMA and Exponential Smoothing. The complexity of today's sales data, with its irregular patterns, seasonality, and non-linear trends, may be too much for conventional systems to handle. This research also presents a comparative study of sales forecasting techniques, focusing on the application of NeuralProphet, an extension of the popular Prophet forecasting library that integrates neural networks. To overcome the shortcomings of conventional approaches, NeuralProphet models complex patterns in time series data using Deep Learning (DL). The study utilizes historical sales data from a diverse set of industries, including retail, ecommerce, and manufacturing, to evaluate the performance of NeuralProphet in comparison to traditional forecasting methods. The results showed that Neural prophet showed better results compared to Facebook prophet in terms of Root Mean Square Error (RMSE).