Add details to readme

README.md

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 short_description: A Demo of statforecast methods
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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).