Too Long; Didn't Read
When developing predictive models and algorithms, whether linear regression or <a href="http://www.acheronanalytics.com/acheron-blog/arima-and-ets-forecasting-in-r" target="_blank">ARIMA models</a> it is important to quantify how well the model fits to the future observations. One of the simplest methods of calculating how correct a model is uses the error between the predicted value and the actual value. From there, there are several methodologies that take this difference and further exploit meaning from it. Quantifying the accuracy of an algorithm is an important step to justifying the usage of the algorithm in product.