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99 lines
4.9 KiB
Markdown
Vendored
99 lines
4.9 KiB
Markdown
Vendored
---
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title: autoselect
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description: The autoselect page provides information on how to utilize specific parameters
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for advanced statistical forecasting using models such as AutoARIMA, AutoETS, AutoCES,
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MSTL, and more. It also includes examples of how to use this forecasting tool.
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keywords:
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- autoselect
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- statistical forecast
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- AutoARIMA
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- AutoETS
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- AutoCES
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- MSTL
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- seasonality
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- forecasting
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- performance per model
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---
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
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<HeadTitle title="forecast /autoselect - Reference | OpenBB Terminal Docs" />
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Perform Automatic Statistical Forecast (select best statistical model from AutoARIMA, AutoETS, AutoCES, MSTL, ...)
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### Usage
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```python
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autoselect [--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 Nov 09, 15:23 (🦋) /forecast/ $ load AAPL
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2022 Nov 09, 15:24 (🦋) /forecast/ $ autoselect AAPL
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Cross Validation Time Series 1: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:4700:00, 2.40it/s]
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Forecast: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:0100:00, 1.80s/it]
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Performance per model.
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Best model: AutoETS
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┏━━━━━━━━━━━━━━━┳━━━━━━━━┓
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┃ Model ┃ MAPE ┃
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┡━━━━━━━━━━━━━━━╇━━━━━━━━┩
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│ AutoETS │ 2.91% │
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├───────────────┼────────┤
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│ AutoARIMA │ 2.93% │
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├───────────────┼────────┤
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│ RWD │ 3.04% │
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├───────────────┼────────┤
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│ AutoCES │ 3.15% │
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├───────────────┼────────┤
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│ MSTL │ 3.40% │
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├───────────────┼────────┤
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│ SeasonalNaive │ 4.32% │
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├───────────────┼────────┤
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│ SeasWA │ 8.06% │
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└───────────────┴────────┘
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Actual price: 139.50
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┏━━━━━━━━━━━━┳━━━━━━━━━━━━┓
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┃ Datetime ┃ Prediction ┃
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┡━━━━━━━━━━━━╇━━━━━━━━━━━━┩
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│ 2022-11-09 │ 139.47 │
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├────────────┼────────────┤
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│ 2022-11-10 │ 139.47 │
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├────────────┼────────────┤
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│ 2022-11-11 │ 139.47 │
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├────────────┼────────────┤
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│ 2022-11-14 │ 139.47 │
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├────────────┼────────────┤
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│ 2022-11-15 │ 139.47 │
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└────────────┴────────────┘
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```
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---
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