autoselect
Perform Automatic Statistical Forecast (select best statistical model from AutoARIMA, AutoETS, AutoCES, MSTL, ...)
Usageโ
autoselect [--naive] [-d {AAPL}] [-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]
Parametersโ
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 | AAPL |
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 |
Examplesโ
2022 Nov 09, 15:23 (๐ฆ) /forecast/ $ load AAPL
2022 Nov 09, 15:24 (๐ฆ) /forecast/ $ autoselect AAPL
Cross Validation Time Series 1: 100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 115/115 [00:4700:00, 2.40it/s]
Forecast: 100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 1/1 [00:0100:00, 1.80s/it]
Performance per model.
Best model: AutoETS
โโโโโโโโโโโโโโโโโณโโโโโโโโโ
โ Model โ MAPE โ
โกโโโโโโโโโโโโโโโโโโโโโโโโโฉ
โ AutoETS โ 2.91% โ
โโโโโโโโโโโโโโโโโผโโโโโโโโโค
โ AutoARIMA โ 2.93% โ
โโโโโโโโโโโโโโโโโผโโโโโโโโโค
โ RWD โ 3.04% โ
โโโโโโโโโโโโโโโโโผโโโโโโโโโค
โ AutoCES โ 3.15% โ
โโโโโโโโโโโโโโโโโผโโโโโโโโโค
โ MSTL โ 3.40% โ
โโโโโโโโโโโโโโโโโผโโโโโโโโโค
โ SeasonalNaive โ 4.32% โ
โโโโโโโโโโโโโโโโโผโโโโโโโโโค
โ SeasWA โ 8.06% โ
โโโโโโโโโโโโโโโโโดโโโโโโโโโ
Actual price: 139.50
โโโโโโโโโโโโโโณโโโโโโโโโโโโโ
โ Datetime โ Prediction โ
โกโโโโโโโโโโโโโโโโโโโโโโโโโโฉ
โ 2022-11-09 โ 139.47 โ
โโโโโโโโโโโโโโผโโโโโโโโโโโโโค
โ 2022-11-10 โ 139.47 โ
โโโโโโโโโโโโโโผโโโโโโโโโโโโโค
โ 2022-11-11 โ 139.47 โ
โโโโโโโโโโโโโโผโโโโโโโโโโโโโค
โ 2022-11-14 โ 139.47 โ
โโโโโโโโโโโโโโผโโโโโโโโโโโโโค
โ 2022-11-15 โ 139.47 โ
โโโโโโโโโโโโโโดโโโโโโโโโโโโโ