---
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!