Amazon Forecast
Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts. It analyzes your historical time-series data and automatically selects the right machine learning algorithms to generate accurate forecasts with no machine learning expertise required.
Solid contracts, transparent operations, and an easy start — typically complete on four or five facets with one clear soft spot.
API Evangelist profiles Amazon Forecast the way a machine reads it — 63 machine-readable artifacts across 1 API, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Amazon Forecast scores 55.7/100 (strong), with a separate agent-readiness read of 31/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance. Every facet and dimension name is a link: it opens that measurement's page on APIs.io, where the rating runs across the whole catalog — the exact checks that feed it, how every profiled provider distributes on it, and who is at the top of it.
Kin Score
How this is scored →scored 2026-09-10 · rubric v0.20.0
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How we profile Amazon Forecast
Each block below is one kind of artifact we hold for Amazon Forecast. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 6
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Amazon Forecast Dataset Groups API
Logical groupings of related datasets
Amazon Forecast Datasets API
Dataset management for training data
Amazon Forecast Export Jobs API
Forecast data export to S3
Amazon Forecast Forecasts API
Generated forecast outputs
Amazon Forecast Predictors API
ML models trained on dataset groups
Amazon Forecast Tags API
Resource metadata labels
Postman Collections 1
A runnable collection turns the contract into something a developer can execute in seconds. We profile them because the fastest way to trust an API is to make a real call against it.
Ready-to-run Postman collections for exercising this provider's APIs.
Amazon Forecast API
POSTMANOpen Collections 8
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
API Collection
OPEN COLLECTIONAmazon Forecast Dataset Groups API
OPEN COLLECTIONAmazon Forecast Dataset Groups Datasets API
OPEN COLLECTIONAmazon Forecast Dataset Groups Export Jobs API
OPEN COLLECTIONAmazon Forecast Dataset Groups Forecasts API
OPEN COLLECTIONAmazon Forecast Dataset Groups Predictors API
OPEN COLLECTIONAmazon Forecast Dataset Groups Tags API
OPEN COLLECTIONAmazon Forecast API
OPEN COLLECTIONScroll within the panel for all 8 ·
Arazzo Workflows 9
Real integrations are rarely a single call. Arazzo describes the multi-step sequences — auth, then create, then confirm — so both a human and an agent can follow the choreography, not just the endpoints.
Multi-step API workflows described with the Arazzo specification.
Amazon Forecast End to End Pipeline
Create a dataset, dataset group, predictor, and forecast in a single chained pass.
ARAZZOAmazon Forecast Export Forecast
Create a forecast, wait until ACTIVE, export it to S3, and read its tags.
ARAZZOAmazon Forecast Generate Forecast
Create a forecast from a predictor, poll until ACTIVE, and tag it.
ARAZZOAmazon Forecast Group Then Train
Create a dataset group, wait until ACTIVE, then train a predictor on it.
ARAZZOAmazon Forecast Predict and Forecast
Train a predictor, wait until ACTIVE, then create a forecast and wait until ACTIVE.
ARAZZOAmazon Forecast Provision Dataset Group
Create a dataset group, poll the listing until it is ACTIVE, and tag it.
ARAZZOAmazon Forecast Register Dataset to Group
Create a dataset, wait until ACTIVE, then create a dataset group containing it.
ARAZZOAmazon Forecast Train Predictor
Create a predictor, poll the listing until it is ACTIVE, and tag it.
ARAZZOScroll within the panel for all 9 ·
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the things the Kin Score reads for access clarity — renamed from commercial clarity in rubric 0.12, because a free statutory interface has access terms and no commercial ones.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Amazon Forecast Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Amazon Forecast Finops
FINOPSFeatures 7
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
AutoML
Automatically evaluates and selects from over 60 ML algorithms to find the best fit for your time-series data.
Probabilistic Forecasts
Generates quantile forecasts (p10, p50, p90) to estimate demand uncertainty and plan inventory buffers.
Domain-Specific Models
Pre-built domain configurations for retail, workforce, traffic, and cloud capacity forecasting.
Related Time Series
Incorporate external factors (price, promotions, holidays) as related time-series data to improve accuracy.
HPO (Hyperparameter Optimization)
Automatic tuning of model hyperparameters to maximize forecast accuracy.
Explainability
Forecast Explainability reports show which features most impact each individual forecast.
S3 Export
Export forecast results to Amazon S3 in CSV format for downstream consumption.
Scroll within the panel for all 7 ·
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Amazon Forecast Context
JSON-LDSpectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Amazon Forecast API Rules
SPECTRALAmazon Forecast API Rules
SPECTRALJSON Schema 5
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
JSON Structure 5
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Amazon Forecast Dataset Group Structure
JSON STRUCTUREAmazon Forecast Dataset Structure
JSON STRUCTUREAmazon Forecast Forecast Structure
JSON STRUCTUREAmazon Forecast Predictor Structure
JSON STRUCTUREAmazon Forecast Tag Structure
JSON STRUCTUREExamples 5
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Security Posture 4
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 6
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Retail Demand Forecasting
Predict item-level sales for inventory planning and replenishment across stores.
Workforce Capacity Planning
Forecast staffing needs for contact centers and seasonal workforce management.
Cloud Resource Forecasting
Predict EC2 capacity requirements to optimize reserved instance purchases.
Supply Chain Optimization
Forecast component and raw material demand to reduce stockouts and carrying costs.
Financial Revenue Forecasting
Project revenue by product, region, and channel for financial planning.
Energy Load Forecasting
Predict electricity load and generation requirements for grid balancing.
Resources
Every other property we hold for Amazon Forecast — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 3
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 12
Pagination, idempotency, versioning, errors, and events
Scroll within the panel for all 12 ·
Build 2
SDKs, sample code, and the tooling you integrate with
Access & Security 4
Authentication, authorization, and security posture
Learn 1
Tutorials, courses, talks, and written guidance
Operate 3
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/amazon-forecast · machine-readable index on apis.io
This is an independent, third-party profile of Amazon Forecast, published by API Evangelist. We do not operate, host, resell, or support these APIs, and we are not affiliated with or endorsed by the company unless stated above. Everything here is built from publicly available information — the company's own site, developer portal, documentation, public repositories, and the specifications it publishes for public use. Nothing is obtained by breaching a system, defeating an access control, or using credentials.
The Kin Score and Agent Readiness rating are independently calculated assessments of a company's public API artifacts, scored against a published rubric. They are not certifications, endorsements, security assessments, or audits.
Corrections, re-scores, and removal are free — no partnership or purchase required, and you do not need to justify the request. A removed company is recorded as unrated, never scored zero for having asked. Acknowledgement within one business day; removal within two.
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