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--- |
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title: SimpleTS |
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emoji: 📈 |
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colorFrom: green |
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colorTo: blue |
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sdk: docker |
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app_port: 8501 |
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tags: |
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- streamlit |
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- time-series |
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- forecasting |
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- prophet |
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- arima |
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- holt-winters |
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pinned: false |
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short_description: Analyze and forecast time series — no code required. |
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--- |
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# SimpleTS |
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Interactive Streamlit dashboard for time series analysis and forecasting. Upload your CSV or use built-in demos. |
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## Author |
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Eduardo Nacimiento García |
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📧 enacimie@ull.edu.es |
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📜 Apache 2.0 License |
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## Features |
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- Upload CSV or use daily/monthly/weekly demo datasets |
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- Automatic date parsing and time series setup |
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- Stationarity test (ADF) |
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- Seasonal decomposition (trend, seasonality, residuals) |
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- ACF & PACF plots |
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- Forecasting models: |
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- Holt-Winters Exponential Smoothing |
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- ARIMA (configurable p,d,q) |
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- Prophet (by Meta) |
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- Metrics: MAE, MSE, RMSE |
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- Interactive future forecasting |
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- Plotly visualizations |
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## Demo Datasets |
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Three built-in demos: |
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- **Daily** (1 year, 365 points) |
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- **Weekly** (2 years, 104 points) |
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- **Monthly** (4 years, 48 points) |
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## Deployment |
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Ready for [Hugging Face Spaces](https://huggingface.co/spaces) (free tier). |
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> ⚠️ Uses `sdk: docker` — include `Dockerfile`. |
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## Requirements |
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- Python 3.8+ |
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- Streamlit, pandas, numpy, plotly, statsmodels, prophet, scikit-learn |
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--- |
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💡 Tip: After uploading, select date/value columns → analyze stationarity & seasonality → choose model → forecast future values! |
