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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>
3.8 KiB
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3.8 KiB
Vendored
title, description, keywords
| title | description | keywords | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| rwd | This page details the procedure for performing a Random Walk with Drift (RWD) forecast using the statsforecast tool in Python. Complete with parameter listings, usage instructions, and examples, the guidance facilitates accurate and efficient data analysis. |
|
import HeadTitle from '@site/src/components/General/HeadTitle.tsx';
Perform Random Walk with Drift forecast: https://nixtla.github.io/statsforecast/models.html#randomwalkwithdrift
Usage
rwd [--naive] [-d {}] [-c TARGET_COLUMN] [-n N_DAYS] [-s {N,A,M}] [-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 |
| 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, 18:43 (🦋) /stocks/ $ load AAPL
2022 Nov 07, 18:43 (🦋) /stocks/ $ forecast
2022 Nov 07, 18:43 (🦋) /forecast/ $ rwd AAPL
Cross Validation Time Series 1: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 115/115 [00:0200:00, 49.97it/s]
Forecast: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:0000:00, 15887.52it/s]
RWD obtains MAPE: 2.98%
Actual price: 138.38
┏━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Datetime ┃ Prediction ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ 2022-11-07 │ 138.47 │
├────────────┼────────────┤
│ 2022-11-08 │ 138.57 │
├────────────┼────────────┤
│ 2022-11-09 │ 138.66 │
├────────────┼────────────┤
│ 2022-11-10 │ 138.76 │
├────────────┼────────────┤
│ 2022-11-11 │ 138.85 │
└────────────┴────────────┘