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DOC: Improve explanation for exog forecasts
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arch/univariate/base.py

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@@ -1003,6 +1003,8 @@ def forecast(
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with a horizon of 10, then the input can be (1, 10). Alternatively,
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if the original data had 1000 observations, then the input can be
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(1000, 10), and only the final row is used to produce forecasts.
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When using an (nobs, horizon) array, the values much be aligned
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so that all values in row t are all out-of-sample at time-t.
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* A dictionary of 2-d array-like: This format is identical to the
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previous except that the dictionary keys must match the names of
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the exog variables. Requires that the exog variables were
@@ -1014,7 +1016,7 @@ def forecast(
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Due to the complexity required to accommodate all scenarios, please
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see the example notebook that demonstrates the valid formats for
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x.
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x, and discusses alignment.
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.. versionadded:: 4.19
10201022

examples/univariate_forecasting_with_exogenous_variables.ipynb

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examples/univariate_volatility_forecasting.ipynb

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@@ -711,7 +711,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.7"
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"version": "3.9.20"
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}
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},
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"nbformat": 4,

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