# What is the difference between using a time series model and using i.e the naïve approach for forecasting?

I was reading about forecasting at Wikipedia: Forecasting and I noticed that in the publication they separate the Naïve, Average and Drift approach from the Time series methods (which involve AR, MA, ARMA, ARIMA, exponential smoothing models...). This is a little confusing for me, because I understood that all of these methods were Time series methods. What is the difference between these forecasting approaches and the "Time series methods"?.

2. Define a complete probabilistic model for the dynamics, typically in the form of the transition law $$p(Y_t|Y_1, ..., Y_{t-1})$$. In that case, a forecast function can be derived from this probabilistic model, usually as the solution to an optimization problem (e.g. the conditional expectation is optimal in a least squares sense). Models in this category allow for a natural quantification of forecast uncertainty in the form of prediction intervals. ARIMA models fall in this category.