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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>
2023-10-30 21:01:29 +00:00

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.
AutoARIMA
Forecasting
Predictive modeling
ARIMA models
Time Series Analysis
Machine Learning
Algorithm
Data Modeling
Statistical Analysis

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     │
└────────────┴────────────┘