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* Update generate_terminal_markdown.py * updates * update workflows * Automated Change * tests * tests * Automated Change * Update test_generate_docs.py * Automated Change * updates * Automated Change * Update gen-docs.yml * Update test.yml * Automated Change * Update test.yml * Automated Change * Update generate_sdk_markdown.py * Automated Change * test * Automated Change * testtt23 * Automated Change * Update generate_sdk_markdown.py * Automated Change * Automated Change * added empting reference folders before autogen docs * updates * Automated Change * update tests gitactions * Automated Change * up * Automated Change * Automated Change * update pytest * Automated Change * tests * Automated Change * examples update * Automated Change * Update generate_sdk_markdown.py * Automated Change * Update test.yml * Update generate_sdk_markdown.py * strip * Automated Change * Workflow : check test update * Automated Change * Automated Change * Automated Change * Update test.yml * Automated Change * Update test.yml * Automated Change * Automated Change * up * Automated Change * Update generate_sdk_markdown.py * Automated Change * update workflows * fix guides * testing * Update gh-pages.yml * Update gh-pages.yml Co-authored-by: tehcoderer <tehcoderer@users.noreply.github.com> Co-authored-by: Jeroen Bouma <jer.bouma@gmail.com> Co-authored-by: Chavithra PARANA <chavithra@gmail.com> Co-authored-by: Chavithra <Chavithra@users.noreply.github.com> Co-authored-by: jose-donato <43375532+jose-donato@users.noreply.github.com> Co-authored-by: jose-donato <jose-donato@users.noreply.github.com>
3.4 KiB
Vendored
3.4 KiB
Vendored
title, description
| title | description |
|---|---|
| mstl | OpenBB Terminal Function |
mstl
Perform Multiple Seasonalities and Trend using Loess (MSTL) forecast: https://nixtla.github.io/statsforecast/examples/multipleseasonalities.html
Usage
mstl [--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]
Parameters
| Name | Description | Default | Optional | Choices |
|---|---|---|---|---|
| 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 |
| seasonal | Seasonality: N: None, A: Additive, M: Multiplicative. | A | True | N, A, M |
| seasonal_periods | Seasonal periods: 4: Quarterly, 7: Daily | 7 | True | None |
| start_window | Start point for rolling training and forecast window. 0.0-1.0 | 0.85 | 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 |
| residuals | Show the residuals for the model. | False | True | None |
| forecast_only | Do not plot the historical data without forecasts. | False | True | None |
| export_pred_raw | Export predictions to a csv file. | False | True | None |
Examples
2022 Nov 07, 18:16 (🦋) /forecast/ $ mstl AAPL
Cross Validation Time Series 1: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:0100:00, 103.78it/s]
Forecast: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:0000:00, 19.19it/s]
MSTL obtains MAPE: 3.37%
Actual price: 138.38
┏━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Datetime ┃ Prediction ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 2022-11-07 │ 137.45 │
├────────────┼────────────┤
│ 2022-11-08 │ 142.27 │
├────────────┼────────────┤
│ 2022-11-09 │ 140.00 │
├────────────┼────────────┤
│ 2022-11-10 │ 141.32 │
├────────────┼────────────┤
│ 2022-11-11 │ 141.36 │
└────────────┴────────────┘