Hi Naren, a time series forecast creates a model that it uses to "predict" historical values as well as to predict future values. So fitted values are the historical predictions, while predictions are the future predictions. For example, your forecast model might calculate a fitted value of 20 units sold on Aug 2019, but you actually sold 15, so the error was 5 units. You want to compare all the fitted values (historical predictions) to the corresponding actual values on each date in order to judge the accuracy of your model. There's lots of different error metrics you can calculate to gauge model accuracy - MSE, MAE, MASE, MAPE, etc.