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Sorry for ignorance. Did you mean to say Simple Linear regression or something else? Do you have any reference for that?
by amrrs 7y ago
Sorry for ignorance. Did you mean to say Simple Linear regression or something else? Do you have any reference for that?
- chroem- 7y agoYes, it's an extension of linear regression. You can incorporate different basis functions to model trend, seasonality, the effects of external regressors, different frequency components, etc. It gives you a lot more control over the forecast model.
- riyadparvez 7y agoAre you referring to Generalized Additive Models (GAM)?
- semi-extrinsic 7y agoI think GAMs are more for the case where you have many underlying input variables and a single resulting response.
- XuMiao 7y agoThis is a less studied senario for time series research: contextual forecasting. With enough contextual information, forecasting doesn't need to squeeze the information from its own history that hard.
- semi-extrinsic 7y agoNot OP, but as an example, imagine you want to fit the time series of measured monthly CO2 at Mauna Loa: https://www.esrl.noaa.gov/gmd/ccgg/trends/ https://www.esrl.noaa.gov/gmd/ccgg/trends/ Just by looking at the graph, you can see that fitting a linear combination of a constant term, t, t2 and sin(w*t) will give you a very accurate model that has five tuning parameters (four weights and one frequency).
- tgv 7y agoHere's an explanation: https://www.youtube.com/watch?v=rVviNyIR-fI https://www.youtube.com/watch?v=rVviNyIR-fI It's basically fitting on non-linear transformations of x.
- clircle 7y agoYou can fit a basis function regression model with least squares.