Sales Forecasting Prediction using Machine Learning

Abstract

This research investigates the use of machine learning methods in improving predictive performance in the automotive sector, with a focus on the Prophet algorithm for car sales forecasting. Using past sales data, Prophet successfully detects trends, seasonality, and external factors affecting car sales. The forecasting starts with careful data preparation to have clean and well-organized historical records. One of the strengths of Prophet is that it can manage complicated time series data that includes several patterns of seasonality as well as holiday effects that are significant in the automotive industry, where demand is influenced by yearly model releases and economic downturns. The produced forecasts help companies maximize marketing efforts, production planning, and inventory stockpiling by giving them insights that can enhance efficiency and decision-making. Second, Prophet's interpretability enables stakeholders to easily decompose forecast components, making data-driven strategic planning possible. Such a method is a major step forward in utilizing machine learning in sales forecasting and provides a robust tool for predicting and understanding automotive market trends.