# "Most forecasting algorithms assumes that each point is independent of one another." If so, how forecasting is being possible?

In stationary, if we want to make forecasting, we have to make our data stationary(On classical methods), I get that, but If every data point is independent of each other, how can we make the prediction of what will be the value of the next time step? I mean there is no pattern, no association, nothing. How a model can estimate what will be the next value?

And while this is the case, Imagine I have data that every time stamp has different values (their meaning is completely independent of each other), and this is a classification problem. (I want to classify each row), based on the assumption of "stationary time series", my values are already stationary, so, can I easily convert this data to the "time series data" and forecast it?

Thanks.

• Intuitively, If your values are stationary, Then it would be following "reversion to mean", So if the last value is very high/very low when compared to mean, Then the next value will be much closer to mean. Isn't that useful for a model to estimate its next value? Oct 14, 2022 at 12:40
• Thank you so much for your response, but i am sorry that i couldn't understand what you mean by "Then it would be following "reversion to mean" Is it possible for you to illustrate or give some examples, please? Oct 14, 2022 at 12:57