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

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mstl	A comprehensive guide for MSTL - Multiple Seasonalities and Trend using Loess forecast analysis in Python. This page includes technical details to understand the parameters for a more accurate prediction, including details on seasonalities and trends. A practical example is provided for clarity.	MSTL forecast Multiple Seasonalities and Trend Predictive Modeling Time Series Forecasting Loess Forecast Seasonality Predictive Analytics Tools

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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]

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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

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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     │
└────────────┴────────────┘

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