README.md

metadata

title: Statsforecast
emoji: 🔥
colorFrom: yellow
colorTo: green
sdk: gradio
sdk_version: 5.23.3
app_file: app.py
pinned: false
short_description: A Demo of statforecast methods

StatsForecast Demo App

This demo application showcases various time series forecasting models from the StatsForecast package.

Features

• Upload your own time series data in CSV format
• Choose from multiple forecasting models:
  • Historical Average
  • Naive
  • Seasonal Naive
  • Window Average
  • Seasonal Window Average
  • AutoETS
  • AutoARIMA
• Configure evaluation strategy:
  • Fixed Window
  • Cross Validation
• View performance metrics (ME, MAE, RMSE, MAPE)
• Visualize forecasts

How to Use

1. Upload a CSV file with time series data containing:

   • unique_id column: Identifier for each time series
   • ds column: Date/timestamp
   • y column: Target values

2. Configure:

   • Frequency (D=daily, H=hourly, M=monthly, etc.)
   • Evaluation strategy and parameters
   • Select models and their parameters

3. Click "Run Forecast" to see results

Sample Data Format

Your CSV should look like this:

unique_id,ds,y
series1,2023-01-01,100
series1,2023-01-02,105
series1,2023-01-03,98
...

About StatsForecast

StatsForecast is a Python library that provides statistical forecasting algorithms for time series data. It is fast and scalable and offers many classical forecasting methods.

For more information, visit Nixtla's StatsForecast repository.