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---
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](https://github.com/Nixtla/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](https://github.com/Nixtla/statsforecast).
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