Hacker News Reader

Yes, 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.

View on HN

Are you referring to Generalized Additive Models (GAM)?

I think GAMs are more for the case where you have many underlying input variables and a single resulting response.

This 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.

Reply on news.ycombinator.com