 Now that we've outlined some examples, let's look at the core differences between traditional time series models and machine learning models for time series forecasting. Traditional time series forecasting is recursive. Now to determine the inbound volume three days from now, the traditional time series way would be that we determine the inbound volume one day from now, use that to determine the two day out prediction, and then use this to determine the three day out prediction. The machine learning approach though, we can forecast this directly. So we would directly know the three day out forecast if our model is trained to do so. So here's the second difference. Time series models are easily extendable.