This paper explores different forecasting techniques to predict sales data for the automobile, Ford Mustang. Companies rely on accurate forecasted data to make the right business decisions and to foresee long-term and short-term market performances. Forecasting data like sales data, demand data and market trends helps companies better manage their resources like cash flow, project funds, project plans, workforce and inventory. Forecasts are usually based on past data, industry-comparisons and market trends. In this project, different time-series forecasting models such as moving average, exponential smoothing, Holt’s double exponential smoothing method, Winter’s triple exponential smoothing method and the ARIMA were utilized. Forecasts were made based on the individual yearly data (non-seasonal) and also on all the yearly data combined (seasonal) in the ARIMA model. Minitab was used to generate forecasts for both Winter’s triple exponential smoothing method as well as the ARIMA model. Further, on computing the mean absolute deviation (MAD), it was found that the best forecasting method for this given set of data was found out to be Holt’s double exponential smoothing method. This study may inspire companies to adopt accurate forecasting techniques for similar data and may also motivate future studies to develop further precise forecasting tools.