In most settings, model fitting is fast enough that there isn’t any issue with re-fitting from scratch. Prophet models can only be fit once, and a new model must be re-fit when new data become available. This PR provides a good illustration of what must be done to implement a custom trend, as does this one that implements a step function trend and this one for a new trend in R.Ī common setting for forecasting is fitting models that need to be updated as additional data come in. To use a trend besides these three built-in trend functions (piecewise linear, piecewise logistic growth, and flat), you can download the source code from github, modify the trend function as desired in a local branch, and then install that local version. Note that if this is used on a time series that doesn’t have a constant trend, any trend will be fit with the noise term and so there will be high predictive uncertainty in the forecast. Changes in seasonality between pre- and post-COVID. Treating COVID-19 lockdowns as a one-off holidays.Prior scale for holidays and seasonality.Seasonalities that depend on other factors.Seasonality, Holiday Effects, And Regressors Specifying the locations of the changepoints.Automatic changepoint detection in Prophet.
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