auto_arima_chapkit

An ARIMA model which automatically chooses hyperparameters using the ARIMA() function from the fable R package, wrapped as a chapkit ML service.

This project was scaffolded using the Chapkit CLI.

Quick Start

Run from GHCR (no build needed)

docker compose -f compose.ghcr.yml up

Build and run locally

docker compose up --build

Or using Make:

make run

The API will be available at:

• API: http://localhost:8000
• API Docs: http://localhost:8000/docs

Development Mode

Install dependencies and run the service locally:

uv sync
uv run python main.py

Project Structure

auto_arima_chapkit/
├── main.py                    # FastAPI app and model configuration
├── scripts/                   # R scripts for training and prediction
│   ├── train.R                # Training script (fable ARIMA)
│   ├── predict.R              # Prediction script
│   └── utils.R                # Shared utility functions
├── example_data/              # Example weekly data (CSV)
├── example_data_monthly/      # Example monthly data (CSV)
├── pyproject.toml             # Python dependencies
├── Dockerfile                 # Docker build configuration
├── compose.yml                # Docker Compose (local build)
├── compose.ghcr.yml           # Docker Compose (GHCR image)
├── Makefile                   # Shortcuts: build, run, run-ghcr
└── .github/workflows/
    ├── ci.yml                 # CI: Docker build + chapkit test
    └── publish-docker.yml     # Publish image to GHCR on push/tag

API Endpoints

Health Check

curl http://localhost:8000/health

Configuration Management

Create a configuration:

curl -X POST http://localhost:8000/api/v1/configs \
  -H "Content-Type: application/json" \
  -d '{
    "name": "my-config",
    "data": {}
  }'

ML Operations

Train a model:

curl -X POST http://localhost:8000/api/v1/ml/\$train \
  -H "Content-Type: application/json" \
  -d '{
    "config_id": "YOUR_CONFIG_ID",
    "data": { ... }
  }'

Make predictions:

curl -X POST http://localhost:8000/api/v1/ml/\$predict \
  -H "Content-Type: application/json" \
  -d '{
    "model_id": "YOUR_MODEL_ID",
    "future": { ... }
  }'

Makefile targets

Target	Description
make build	Build the Docker image locally
make run	Build and run the image on port 8000
make run-ghcr	Pull and run the prebuilt GHCR image

Documentation

• Chapkit Documentation
• FastAPI Documentation