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Amazon Forecast - Runbook & Reference

中文 · GitHub source Facts verified against official AWS documentation: 2026-08-19

Overview

Amazon Forecast is a fully managed time-series forecasting service that uses statistical and machine learning algorithms to predict future values from historical data, with no ML experience required. Note: Amazon Forecast is no longer available to new customers; existing customers can continue using the service as normal.

Key concepts

  • Time-series forecasting: predict future data points (demand, traffic, capacity, financial metrics) based on historical series.
  • Datasets: import time-series data (and related item/user metadata) through the console, API, CLI, or SDK.
  • Predictor: a trained forecasting model built from your datasets; Forecast automates algorithm selection and training.
  • Forecast generation: produce forecasts for the horizon you define; evaluate against backtests for accuracy.
  • Features: automated ML, state-of-the-art algorithms, missing-value handling, and built-in feature-engineered datasets (for example, holidays).
  • Use cases: retail demand planning, supply chain, resource planning, operational planning (web traffic, server capacity).

Common operations (AWS CLI)

# Create a dataset group, import data, and create a predictor
aws forecast create-dataset-group --dataset-group-name retail \
  --domain RETAIL --dataset-arns <dataset-arn>
aws forecast create-dataset-import-job --dataset-arn <dataset-arn> \
  --dataset-import-job-name initial \
  --data-source '{"S3Config":{"Path":"s3://bucket/data","RoleArn":"arn:aws:iam::123456789012:role/forecast-role"}}'
aws forecast create-auto-predictor --predictor-name demand \
  --forecast-horizon 30 --data-config file://data.json

# Generate and retrieve forecasts
aws forecast create-forecast --forecast-name demand-30 \
  --predictor-arn <predictor-arn>
aws forecast describe-forecast --forecast-arn <forecast-arn>

Best practices

  • Prepare clean, regular time-series data (timestamps, item IDs, target values) and use related time-series metadata when available.
  • Use AutoPredictor for automated algorithm selection; validate accuracy with backtests before production.
  • Choose a forecast horizon that matches your planning cycle (for example, 30 or 90 days).
  • Handle missing values deliberately; Forecast provides filling methods.
  • Store forecasts in S3/Redshift for downstream planning systems.
  • Existing customers: track the service status; plan alternatives if you start new forecasting initiatives.

Troubleshooting

Symptom Checks and fixes
Dataset import fails Check CSV format, S3 permissions, and the dataset schema.
Predictor training fails Verify data frequency, item count limits, and horizon settings.
Forecast accuracy poor Add related time-series data, clean outliers, and evaluate with backtest metrics.
Cannot onboard new account Forecast is closed to new customers; use documented alternatives.
Costs higher than expected Forecast charges depend on generated forecasts, storage, and training hours; review usage.

Limits

Datasets, predictors, and forecasts per account and dataset sizes have quotas; service onboarding is limited to existing customers. See the Amazon Forecast endpoints and quotas page for current values.

Official references