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85 lines
4.4 KiB
Markdown
Vendored
85 lines
4.4 KiB
Markdown
Vendored
---
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title: linregr
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description: This documentation page details how to use and implement the 'linregr'
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function for performing linear regression forecasts. It includes descriptive parameters,
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usage examples, and result interpretations.
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keywords:
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- docusaurus
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- linear regression
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- forecasting
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- linregr
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- parameters
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- usage
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- examples
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- metadata
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- data science
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- machine learning
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---
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
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<HeadTitle title="forecast /linregr - Reference | OpenBB Terminal Docs" />
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Perform a linear regression forecast: https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html
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### Usage
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```python
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linregr [--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, 11:03 (🦋) /forecast/ $ linregr GME
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100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:0700:00, 15.10it/s]
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Logistic Regression model obtains MAPE: 10.85%
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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 │ $ 144.41 │
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├─────────────────────┼────────────┤
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│ 2022-07-20 00:00:00 │ $ 142.69 │
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├─────────────────────┼────────────┤
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│ 2022-07-21 00:00:00 │ $ 140.94 │
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├─────────────────────┼────────────┤
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│ 2022-07-22 00:00:00 │ $ 139.89 │
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├─────────────────────┼────────────┤
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│ 2022-07-25 00:00:00 │ $ 136.04 │
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└─────────────────────┴────────────┘
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```
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---
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