mirror of
https://github.com/saymrwulf/prophet.git
synced 2026-07-30 20:18:11 +00:00
Regenerate documentation #1212
This commit is contained in:
parent
3ef1d66d93
commit
1f6170491d
1 changed files with 4 additions and 4 deletions
|
|
@ -79,7 +79,7 @@ Above we have included the superbowl days as both playoff games and superbowl ga
|
|||
|
||||
|
||||
|
||||
Once the table is created, holiday effects are included in the forecast by passing them in with the `holidays` argument. Here we do it with the Peyton Manning data from the Quickstart:
|
||||
Once the table is created, holiday effects are included in the forecast by passing them in with the `holidays` argument. Here we do it with the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html):
|
||||
|
||||
|
||||
```R
|
||||
|
|
@ -316,7 +316,7 @@ fig = m.plot_components(forecast)
|
|||
|
||||
|
||||
|
||||
Seasonalities are estimated using a partial Fourier sum. See [the paper](https://peerj.com/preprints/3190/) for complete details, and [this figure on Wikipedia](https://en.wikipedia.org/wiki/Fourier_series#/media/File:Fourier_Series.svg) for an illustration of how a partial Fourier sum can approximate an aribtrary periodic signal. The number of terms in the partial sum (the order) is a parameter that determines how quickly the seasonality can change. To illustrate this, consider the Peyton Manning data from the Quickstart. The default Fourier order for yearly seasonality is 10, which produces this fit:
|
||||
Seasonalities are estimated using a partial Fourier sum. See [the paper](https://peerj.com/preprints/3190/) for complete details, and [this figure on Wikipedia](https://en.wikipedia.org/wiki/Fourier_series#/media/File:Fourier_Series.svg) for an illustration of how a partial Fourier sum can approximate an aribtrary periodic signal. The number of terms in the partial sum (the order) is a parameter that determines how quickly the seasonality can change. To illustrate this, consider the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html). The default Fourier order for yearly seasonality is 10, which produces this fit:
|
||||
|
||||
|
||||
```R
|
||||
|
|
@ -370,7 +370,7 @@ The inputs to this function are a name, the period of the seasonality in days, a
|
|||
|
||||
|
||||
|
||||
As an example, here we fit the Peyton Manning data from the Quickstart, but replace the weekly seasonality with monthly seasonality. The monthly seasonality then will appear in the components plot:
|
||||
As an example, here we fit the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html), but replace the weekly seasonality with monthly seasonality. The monthly seasonality then will appear in the components plot:
|
||||
|
||||
|
||||
```R
|
||||
|
|
@ -400,7 +400,7 @@ In some instances the seasonality may depend on other factors, such as a weekly
|
|||
|
||||
|
||||
|
||||
Consider the Peyton Manning example from the Quickstart. The default weekly seasonality assumes that the pattern of weekly seasonality is the same throughout the year, but we'd expect the pattern of weekly seasonality to be different during the on-season (when there are games every Sunday) and the off-season. We can use conditional seasonalities to construct separate on-season and off-season weekly seasonalities.
|
||||
Consider the Peyton Manning example from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html). The default weekly seasonality assumes that the pattern of weekly seasonality is the same throughout the year, but we'd expect the pattern of weekly seasonality to be different during the on-season (when there are games every Sunday) and the off-season. We can use conditional seasonalities to construct separate on-season and off-season weekly seasonalities.
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue