Measurement is the first step that leads to control and eventually improvement.

H. James Harrington

In many business applications, the ability to plan ahead is paramount and in the majority of such scenarios, we use forecasts to help us plan ahead. For eg., If I run a retail store_, how many boxes of that shampoo should I order today?_ Look at the Forecast. Will I achieve my financial targets by the end of the year? Let’s forecast and make adjustments if necessary. If I run a bike rental firm, how many bikes do I need to keep at a metro station tomorrow at 4pm?

If for all of these scenarios, we are taking actions based on the forecast, we should also have an idea about how good those forecasts are. In classical statistics or machine learning, we have a few general loss functions, like the squared error or the absolute error. But because of the way Time Series Forecasting has evolved, there are a lot more ways to assess your performance.

In this blog post, let’s explore the different Forecast Error measures through experiments and understand the drawbacks and advantages of each of them.

#metrics #time-series-analysis #machine-learning #forecast