autoets

Perform Automatic ETS (Error, Trend, Seasonality) forecast: https://nixtla.github.io/statsforecast/examples/getting_started_with_auto_arima_and_ets.html

Usage

autoets [--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	Parameter	Description	Default	Optional	Choices
naive	--naive	Show the naive baseline for a model.	False	True	None
target_dataset	-d --dataset	The name of the dataset you want to select	None	True	None
target_column	-c --target-column	The name of the specific column you want to use	close	True	None
n_days	-n --n-days	prediction days.	5	True	None
seasonal	-s --seasonal	Seasonality: N: None, A: Additive, M: Multiplicative.	A	True	N, A, M
seasonal_periods	-p --periods	Seasonal periods: 4: Quarterly, 7: Daily	7	True	None
start_window	-w --window	Start point for rolling training and forecast window. 0.0-1.0	0.85	True	None
s_end_date	--end	The end date (format YYYY-MM-DD) to select for testing	None	True	None
s_start_date	--start	The start date (format YYYY-MM-DD) to select for testing	None	True	None
residuals	--residuals	Show the residuals for the model.	False	True	None
forecast_only	--forecast-only	Do not plot the historical data without forecasts.	False	True	None
export_pred_raw	--export-pred-raw	Export predictions to a csv file.	False	True	None

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Examples

2022 Oct 21, 18:20 (🦋) /forecast/ $ load AAPL

2022 Oct 21, 18:21 (🦋) /forecast/ $ autoets AAPL



   Actual price: 143.39
┏━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Datetime   ┃ Prediction ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 2022-10-21 │ 143.42     │
├────────────┼────────────┤
│ 2022-10-24 │ 143.42     │
├────────────┼────────────┤
│ 2022-10-25 │ 143.42     │
├────────────┼────────────┤
│ 2022-10-26 │ 143.42     │
├────────────┼────────────┤
│ 2022-10-27 │ 143.42     │
└────────────┴────────────┘

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