OpenBB/website/content/terminal/reference/forecast/regr.md

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title	description	keywords
regr	This page provides a comprehensive guide on how to carry out a regression forecast using regr function in darts.models. Parameters such as past covariates, prediction days, train split, target dataset, and output chunk length are explained in great detail. Examples and usage documentation are also provided to aid in understanding.	regression forecast darts.models forecasting model regr function regr parameters past covariates prediction days train split forecast chunk length forecasting tool

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Perform a regression forecast: https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html

Usage

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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Parameters

Name	Description	Default	Optional	Choices
past_covariates	Past covariates(columns/features) in same dataset. Comma separated.	None	True	None
all_past_covariates	Adds all rows as past covariates except for date and the target column.	False	True	None
naive	Show the naive baseline for a model.	False	True	None
target_dataset	The name of the dataset you want to select	None	True	None
target_column	The name of the specific column you want to use	close	True	None
n_days	prediction days.	5	True	None
train_split	Start point for rolling training and forecast window. 0.0-1.0	0.85	True	None
output_chunk_length	The length of the forecast of the model.	5	True	None
s_end_date	The end date (format YYYY-MM-DD) to select for testing	None	True	None
s_start_date	The start date (format YYYY-MM-DD) to select for testing	None	True	None
lags	Lagged target values used to predict the next time step.	14	True	None
residuals	Show the residuals for the model.	False	True	None
forecast_only	Do not plot the historical data without forecasts.	False	True	None
explainability_raw	Prints out a raw dataframe showing explainability results.	False	True	None
export_pred_raw	Export predictions to a csv file.	False	True	None

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Examples

2022 Jul 23, 10:36 (🦋) /forecast/ $ load GME_20220719_123734.csv -a GME

2022 Jul 23, 11:08 (🦋) /forecast/ $ regr GME
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:0100:00, 111.85it/s]
Regression model obtains MAPE: 12.34%



       Actual price: $ 146.64
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Datetime            ┃ Prediction ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 2022-07-19 00:00:00 │ $ 143.74   │
├─────────────────────┼────────────┤
│ 2022-07-20 00:00:00 │ $ 144.90   │
├─────────────────────┼────────────┤
│ 2022-07-21 00:00:00 │ $ 142.41   │
├─────────────────────┼────────────┤
│ 2022-07-22 00:00:00 │ $ 133.50   │
├─────────────────────┼────────────┤
│ 2022-07-25 00:00:00 │ $ 134.24   │
└─────────────────────┴────────────┘

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