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