Time Series Forecasting using Autoregression Model

This sample uses functions to forecast temperatures based on a series of temperature data. It uses statsmodel autoregression to retrain the data.

Getting Started

Deploy to Azure


  • Install Python 3.6+
  • Install Functions Core Tools
  • Install Docker
  • Note: If run on Windows, use Ubuntu WSL to run deploy script


  • Click Deploy to Azure Button to deploy resources

Deploy to Azure



  • Upload the csv dataset to the forecastinput container blob either through portal or through following Azure CLI
az storage blob upload --container-name forecastinput --account-name {storageName} -f {dataset} -n daily-minimum-temperatures.csv
  • Send the following body in a HTTP POST request as a query param where name: Input CSV file result: Forecast output graph image

Local Testing

For any local testing, use the sample local.settings.json and host.json, create virtual environment and run func host start