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77 lines
4.0 KiB
Markdown
Vendored
77 lines
4.0 KiB
Markdown
Vendored
---
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title: mstl
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description: A comprehensive guide for MSTL - Multiple Seasonalities and Trend using
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Loess forecast analysis in Python. This page includes technical details to understand
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the parameters for a more accurate prediction, including details on seasonalities
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and trends. A practical example is provided for clarity.
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keywords:
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- MSTL forecast
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- Multiple Seasonalities and Trend
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- Predictive Modeling
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- Time Series Forecasting
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- Loess Forecast
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- Seasonality
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- Predictive Analytics Tools
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---
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import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
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<HeadTitle title="forecast /mstl - Reference | OpenBB Terminal Docs" />
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Perform Multiple Seasonalities and Trend using Loess (MSTL) forecast: https://nixtla.github.io/statsforecast/examples/multipleseasonalities.html
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### Usage
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```python
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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]
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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 07, 18:16 (🦋) /forecast/ $ mstl AAPL
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Cross Validation Time Series 1: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:0100:00, 103.78it/s]
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Forecast: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:0000:00, 19.19it/s]
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MSTL obtains MAPE: 3.37%
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Actual price: 138.38
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┏━━━━━━━━━━━━┳━━━━━━━━━━━━┓
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┃ Datetime ┃ Prediction ┃
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┡━━━━━━━━━━━━╇━━━━━━━━━━━━┩
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│ 2022-11-07 │ 137.45 │
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├────────────┼────────────┤
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│ 2022-11-08 │ 142.27 │
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├────────────┼────────────┤
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│ 2022-11-09 │ 140.00 │
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├────────────┼────────────┤
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│ 2022-11-10 │ 141.32 │
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├────────────┼────────────┤
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│ 2022-11-11 │ 141.36 │
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└────────────┴────────────┘
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```
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---
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