https://github.com/dbolotov/ts_smoothing_visualizer

Streamlit app for visualizing and comparing time series smoothing methods on real and synthetic datasets.
https://github.com/dbolotov/ts_smoothing_visualizer

data-science data-visualization streamlit time-series

Last synced: 11 months ago
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Streamlit app for visualizing and comparing time series smoothing methods on real and synthetic datasets.

• Host: GitHub
• URL: https://github.com/dbolotov/ts_smoothing_visualizer
• Owner: dbolotov
• Created: 2025-06-26T16:26:31.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2025-06-26T18:15:26.000Z (12 months ago)
• Last Synced: 2025-06-26T19:22:08.815Z (12 months ago)
• Topics: data-science, data-visualization, streamlit, time-series
• Language: Python
• Homepage: https://timeseriessmoothing.streamlit.app/
• Size: 394 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Time Series Smoothing: an Interactive Visualizer

[![Try the App](https://img.shields.io/badge/TRY%20THE%20APP-FF4B4B)](https://timeseriessmoothing.streamlit.app/)

This repository contains the code for a Streamlit app that visualizes time series smoothing techniques. It includes both real and synthetic datasets and lets you compare how different methods behave with adjustable parameters.

*Note: The app was developed while writing this Medium article: [Six Approaches to Time Series Smoothing](https://medium.com/@dmitriy.bolotov/six-approaches-to-time-series-smoothing-cc3ea9d6b64f)*

**Features**

- Adjustable smoothing parameters

- Visual comparison across methods

- 5 datasets

**Supported methods**: Moving Average, Exponential Moving Average, Savitzky-Golay, LOESS, Gaussian Filter, Kalman Filter

## Datasets

This project uses a mix of real-world and synthetic datasets. Below are the sources and licensing information:

- **Sunspots**  

  Daily total sunspot numbers from [SILSO](https://www.sidc.be/SILSO/datafiles). Licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).

- **Humidity (RH)** and **Wind Speed (WV)**  

  Weather time series from [Weather Long-term Time Series Forecasting](https://www.kaggle.com/datasets/alistairking/weather-long-term-time-series-forecasting) on Kaggle. Licensed under the [MIT License](https://www.mit.edu/~amini/LICENSE.md).

- **Noisy Sine**  

  Synthetic noisy sine wave, created for this project.

- **Process Anomalies**  

  Synthetic dataset simulating different industrial operating modes and injected anomalies, created for this project.