Exploring Time Series Analysis Techniques for Sales Forecasting

Sales forecasting is a decisive task for businesses, as it enables them to make important decisions about production, inventory, and marketing strategies. Time series analysis is a tackle for sales forecasting, as it allows us to analyze and model data based on time-dependent patterns. In this paper, we explore different time series analysis techniques and their application to sales forecasting. We use a real-world sales dataset (retail) to demonstrate the use of various time series techniques such as decomposition, auto-correlation, and lag features. This report presents a solution for a case study in which we forecast the sales of retail stores. It supports strategic decisions on three levels: the featuring of data, decomposing the data, and applying the models. We also discuss the significance of feature engineering in time series analysis and demonstrate the time series features such as lag, date time, and windowing (rolling means). Then, we compare the performance of different time series models, such as naive (persistence), Moving Average, ARIMA, and SARIMAX. We conclude that time series analysis techniques are used correctly and can handle powerful tools for businesses to make accurate sales forecasts and make informed decisions.