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78 lines
3.1 KiB
Markdown
Vendored
78 lines
3.1 KiB
Markdown
Vendored
---
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title: autoets
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description: The page provides information on how to use the autoets function for
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automatic Error, Trend, Seasonality (ETS) forecasting. It includes details on the
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usage of this function, its parameters, and an example to illustrate its application.
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keywords:
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- autoets
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- ETS forecasting
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- automatic ETS
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- Error Trend Seasonality
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- forecast
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- parameters
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- prediction
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- seasonality
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---
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
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<HeadTitle title="forecast /autoets - Reference | OpenBB Terminal Docs" />
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Perform Automatic ETS (Error, Trend, Seasonality) forecast: https://nixtla.github.io/statsforecast/examples/getting_started_with_auto_arima_and_ets.html
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### Usage
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```python
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autoets [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] [-s {N,A,M}] [-p SEASONAL_PERIODS] [-w START_WINDOW] [--end S_END_DATE] [--start S_START_DATE] [--residuals] [--forecast-only] [--export-pred-raw]
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```
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---
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## Parameters
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| Name | Description | Default | Optional | Choices |
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| ---- | ----------- | ------- | -------- | ------- |
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| naive | Show the naive baseline for a model. | False | True | None |
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| target_dataset | The name of the dataset you want to select | None | True | None |
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| target_column | The name of the specific column you want to use | close | True | None |
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| n_days | prediction days. | 5 | True | None |
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| seasonal | Seasonality: N: None, A: Additive, M: Multiplicative. | A | True | N, A, M |
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| seasonal_periods | Seasonal periods: 4: Quarterly, 7: Daily | 7 | True | None |
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| start_window | Start point for rolling training and forecast window. 0.0-1.0 | 0.85 | True | None |
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| s_end_date | The end date (format YYYY-MM-DD) to select for testing | None | True | None |
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| s_start_date | The start date (format YYYY-MM-DD) to select for testing | None | True | None |
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| residuals | Show the residuals for the model. | False | True | None |
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| forecast_only | Do not plot the historical data without forecasts. | False | True | None |
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| export_pred_raw | Export predictions to a csv file. | False | True | None |
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---
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## Examples
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```python
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2022 Oct 21, 18:20 (🦋) /forecast/ $ load AAPL
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2022 Oct 21, 18:21 (🦋) /forecast/ $ autoets AAPL
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Actual price: 143.39
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┏━━━━━━━━━━━━┳━━━━━━━━━━━━┓
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┃ Datetime ┃ Prediction ┃
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┡━━━━━━━━━━━━╇━━━━━━━━━━━━┩
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│ 2022-10-21 │ 143.42 │
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├────────────┼────────────┤
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│ 2022-10-24 │ 143.42 │
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├────────────┼────────────┤
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│ 2022-10-25 │ 143.42 │
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├────────────┼────────────┤
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│ 2022-10-26 │ 143.42 │
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├────────────┼────────────┤
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│ 2022-10-27 │ 143.42 │
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└────────────┴────────────┘
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```
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---
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