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86 lines
4.5 KiB
Markdown
Vendored
86 lines
4.5 KiB
Markdown
Vendored
---
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title: regr
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description: This page provides a comprehensive guide on how to carry out a regression
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forecast using regr function in darts.models. Parameters such as past covariates,
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prediction days, train split, target dataset, and output chunk length are explained
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in great detail. Examples and usage documentation are also provided to aid in understanding.
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keywords:
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- regression forecast
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- darts.models
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- forecasting model
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- regr function
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- regr parameters
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- past covariates
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- prediction days
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- train split
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- forecast chunk length
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- forecasting tool
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---
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
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<HeadTitle title="forecast /regr - Reference | OpenBB Terminal Docs" />
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Perform a regression forecast: https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html
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### Usage
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```python
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regr [--past-covariates PAST_COVARIATES] [--all-past-covariates] [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] [-t TRAIN_SPLIT] [-o OUTPUT_CHUNK_LENGTH] [--end S_END_DATE] [--start S_START_DATE] [--lags LAGS] [--residuals] [--forecast-only] [--explainability-raw] [--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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| past_covariates | Past covariates(columns/features) in same dataset. Comma separated. | None | True | None |
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| all_past_covariates | Adds all rows as past covariates except for date and the target column. | False | True | None |
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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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| train_split | Start point for rolling training and forecast window. 0.0-1.0 | 0.85 | True | None |
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| output_chunk_length | The length of the forecast of the model. | 5 | 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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| lags | Lagged target values used to predict the next time step. | 14 | 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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| explainability_raw | Prints out a raw dataframe showing explainability results. | 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 Jul 23, 10:36 (🦋) /forecast/ $ load GME_20220719_123734.csv -a GME
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2022 Jul 23, 11:08 (🦋) /forecast/ $ regr GME
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100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:0100:00, 111.85it/s]
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Regression model obtains MAPE: 12.34%
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Actual price: $ 146.64
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┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
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┃ Datetime ┃ Prediction ┃
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┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
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│ 2022-07-19 00:00:00 │ $ 143.74 │
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├─────────────────────┼────────────┤
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│ 2022-07-20 00:00:00 │ $ 144.90 │
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├─────────────────────┼────────────┤
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│ 2022-07-21 00:00:00 │ $ 142.41 │
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├─────────────────────┼────────────┤
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│ 2022-07-22 00:00:00 │ $ 133.50 │
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├─────────────────────┼────────────┤
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│ 2022-07-25 00:00:00 │ $ 134.24 │
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└─────────────────────┴────────────┘
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```
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---
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