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0 pointscivilized2y ago0 comments

Many of the most important time series prediction models are called "autoregressive", meaning they are regression models predicting the target from (prior values of) itself. This suggests that statisticians don't really share the view that these domains are distinct, or that regression models should only predict with different variables from the target.

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hackerlight2y ago· 2 in thread

Right, AR(n) is a regression model, as are models which take only exogenous variables.

My question is this. According to definitions, can the latter (f(X_t) = y_t) be a time series model if each row of data is a time step? It doesn't have any autoregressive terms in X, so I don't know if it categorically is a time-series model.

Not that this question even matters, it's purely a taxonomy/terminology question.

nerdponx2y ago

Yes, it is. A time series model is any model where the data varies over time; that is, a time series model is any model of time series data. And timeseries data is broadly anything where the data for a single thing/entity varies over time. There are no strict definitions here, just common conventions.

hackerlight2y ago

Okay. And we can also say that there's some time series models that aren't regression models, right? For example, Kalman Filter is a "model" of a time series but isn't a regression.

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nerdponx2y ago

Correct. In terms of what "kind" of model it is, it's all just a variation of the same linear model, y = bx.

That said, there are a lot of special considerations involved with timeseries data. There is a large number of specialized tools, techniques, and model families dedicated to time series modeling, which don't make sense to use for other kinds of problems. And all of those special time series tools exist to solve problems that do not arise in other modeling situations. So in practice, times series modeling is a distinct specialization from other kinds of modeling.

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