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* add first iteration of content for Terminal Pro * Update index.md * Update dashboards.md * Update folders.md * Update grouping.md * Update report.md * Update templates.md * Update quick-start.md * Update index.md * Update index.md * Update home.md * Update news.md * Update index.md * Update index.md * Update index.md * Update data-provider.md * Update chat-with-widget.md * Update data-manipulation.md * Update forecasting.md * fix: images self closing tags * fix: only pro visitors can see pro * inter font * improve SEO content and restructuring * add new package for toggle on tutorials * move folder around * improve main page of each prod * small updates and improvements * fix logo above * small improvement in headtitle for portfolio funcs * fix typos from OpenAI's GPT-4 * fix _category_ json * remove generic SEO words * add script that generates SEO for documentation * add missing file * remove sdk warning message * fix links for andrew --------- Co-authored-by: jose-donato <zmcdonato@gmail.com> Co-authored-by: jose-donato <43375532+jose-donato@users.noreply.github.com> Co-authored-by: andrewkenreich <andrew.kenreich@gmail.com>
4.1 KiB
Vendored
4.1 KiB
Vendored
title, description, keywords
| title | description | keywords | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| autoarima | The AutoARIMA documentation page provides an extensive guide on how the Automatic ARIMA forecast works. Details include command usage, various parameters, and examples demonstrating how the forecast performs. This page is ideal for those seeking to understand predictive modeling, time-series analysis, and the application of ARIMA models. |
|
import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
Perform Automatic ARIMA forecast: https://nixtla.github.io/statsforecast/examples/getting_started_with_auto_arima_and_ets.html
Usage
autoarima [--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, 16:17 (🦋) /stocks/ $ forecast
2022 Nov 07, 16:17 (🦋) /forecast/ $ autoarima AAPL
Cross Validation Time Series 1: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:2100:00, 5.31it/s]
Forecast: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:0000:00, 1.68it/s]
AutoARIMA obtains MAPE: 2.86%
Actual price: 138.38
┏━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Datetime ┃ Prediction ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 2022-11-07 │ 138.43 │
├────────────┼────────────┤
│ 2022-11-08 │ 138.43 │
├────────────┼────────────┤
│ 2022-11-09 │ 138.43 │
├────────────┼────────────┤
│ 2022-11-10 │ 138.43 │
├────────────┼────────────┤
│ 2022-11-11 │ 138.43 │
└────────────┴────────────┘