Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[DOC] Simple edits to make STLForecaster docstring render proprely #5220

Merged
merged 2 commits into from Sep 16, 2023
Merged

[DOC] Simple edits to make STLForecaster docstring render proprely #5220

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
1eca602
simple edits
hazrulakmal Sep 12, 2023
9f8518d
highligh update & capital i
hazrulakmal Sep 12, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
28 changes: 15 additions & 13 deletions sktime/forecasting/trend.py
View file
Open in desktop
Expand Up @@ -309,22 +309,24 @@ class STLForecaster(BaseForecaster):

The STLForecaster applies the following algorithm, also see [1]_.

in `fit`:
1. use `statsmodels` `STL` [2]_ to decompose the given series `y` into
the three components: `trend`, `season` and `residuals`.
2. fit clones of `forecaster_trend` to `trend`, `forecaster_seasonal` to `season`,
and `forecaster_resid` to `residuals`, using `y`, `X`, `fh` from `fit`.
The forecasters are fitted as clones, stored in the attributes
`forecaster_trend_`, `forecaster_seasonal_`, `forecaster_resid_`.
In `fit`:

1. Use `statsmodels` `STL` [2]_ to decompose the given series `y` into
the three components: `trend`, `season` and `residuals`.
2. Fit clones of `forecaster_trend` to `trend`, `forecaster_seasonal` to `season`,
and `forecaster_resid` to `residuals`, using `y`, `X`, `fh` from `fit`.
The forecasters are fitted as clones, stored in the attributes
`forecaster_trend_`, `forecaster_seasonal_`, `forecaster_resid_`.

In `predict`, forecasts as follows:
1. obtain forecasts `y_pred_trend` from `forecaster_trend_`,
`y_pred_seasonal` from `forecaster_seasonal_`, and
`y_pred_residual` from `forecaster_resid_`, using `X`, `fh`, from `predict`.
2. recompose `y_pred` as `y_pred = y_pred_trend + y_pred_seasonal + y_pred_residual`
3. return `y_pred`

`update` refits entirely, i.e., behaves as `fit` on all data seen so far.
1. Obtain forecasts `y_pred_trend` from `forecaster_trend_`,
`y_pred_seasonal` from `forecaster_seasonal_`, and
`y_pred_residual` from `forecaster_resid_`, using `X`, `fh`, from `predict`.
2. Recompose `y_pred` as `y_pred = y_pred_trend + y_pred_seasonal + y_pred_residual`
3. Return `y_pred`

`update` refits entirely, i.e., behaves as `fit` on all data seen so far.

Parameters
----------
Expand Down