Forecasting future retail sales is one of the most important activities that form the basis for all strategic and planning decisions in effective operations of retail businesses as well as retail supply chains. This chapter illustrates how to best model and forecast retail sales time series that contain both trend and seasonal variations. The effectiveness of data preprocessing such as detrending and deseasonalization on neural network forecasting performance is demonstrated through a case study of two different retail sales: computer store sales and grocery store sales. We show that without data preprocessing neural networks are not able to effectively model retail sales with both trend and seasonality in the data, and either detrending or deseasonalization can greatly improve neural network modeling and forecasting accuracy. A combined approach of detrending and deseasonalization is shown to be the most effective data preprocessing technique that can yield the best forecasting result.