Forecast Accuracy Screencast. So now, what I would like to show you is how to calculate the three forecast accuracy measures that we discussed in the lecture in a spreadsheet. So, we are going to start with the Mean Error. In order to calculate the Mean Error, you need to calculate the error for each period first, which then you can average together. So, we get the error by taking our demand minus our forecast and there we have it in period three demands 24 forecast is 22 our error is 2. Now as we copy this down, what we see is we have positive and negative errors. We over and underforecast and that's okay. That's what we expect from the error. To calculate the mean error, then what we do is we go over here where I've created a spot for it and we take the average of all of these errors that we have, then we have it the mean error in this example is -0.71. Now, let's look at the absolute percent error. So, the absolute percent error needs two parts. One is we need to get an absolute error and then we need to convert it into percent. So, we get the absolute error by taking the absolute value. We already calculated error, so I'll keep using that and then we need to divide it by our demand to get it into a percent form. So, the absolute value of the error that we previously calculated divided by demand and then all I need to do is copy this down over here. And now, I go in to this cell that I prepared here from our mean absolute percent error. All I need to do is here. Average all of these values together and here's our result. On average, we have an error of 13.58%. So every time we create a forecast, we're about almost 14% off. We don't know in which direction on average, we're off. But in general, this is how far we're off on average. So now, we are looking at the squared error and the squared error simply the squared error value. We raise it to the second power. And all I need to do is drag this formula down, captivated overall of these values and then I have another spot where I see it's the average of all the values in the column G. And here we go. Our Mean Squared Error, MSE is 8.86. Now 8.86 in general, we cannot really make a lot of statements on, but on of the key features of the Mean Squared Error is that larger errors. So for example, this forecast error of -7, which is an over forecast of 7, now becomes 49. So in an average, we give it much more than weight than a small error, such as in period 5, 6 and 10, which only has a value of 1 and 1 times 1 stays 1. So what we see here with a Mean Squared Error is an amplification of large errors, which is the ones that we really care about when we forecast. We can deal with small errors, but large errors are the ones that make it much more difficult to perform all of the steps that are required later on. So here, we have the three accuracy measures. The Mean Error, the Mean Absolute Percent Error and the mean Squared Error.