 traditional time series approaches, they can be pretty tough to get right unless you're an expert with time series models. While the machine learning approach is a lot more tractable for people who don't know much about time series forecasting. Although I will say the profit models are an exception here. Now a fourth difference. So time series models, at least the univariate ones, we can't add regressors for which we don't know the future values. While the machine learning models, we can add these regressors that allows us to better fine tune the models. So clearly each has their advantages and disadvantages and depending on the problem you're solving, the data that you have, and the hardware capacity, one of these solutions may be more preferable than the other.