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It includes both real and synthetic datasets and lets you compare how different methods behave with adjustable parameters.\n\n*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)*\n\n**Features**\n- Adjustable smoothing parameters\n- Visual comparison across methods\n- 5 datasets\n\n**Supported methods**: Moving Average, Exponential Moving Average, Savitzky-Golay, LOESS, Gaussian Filter, Kalman Filter\n\n\n## Datasets\n\nThis project uses a mix of real-world and synthetic datasets. Below are the sources and licensing information:\n\n- **Sunspots**  \n  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/).\n\n- **Humidity (RH)** and **Wind Speed (WV)**  \n  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).\n\n- **Noisy Sine**  \n  Synthetic noisy sine wave, created for this project.\n\n- **Process Anomalies**  \n  Synthetic dataset simulating different industrial operating modes and injected anomalies, created for this project.\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdbolotov%2Fts_smoothing_visualizer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdbolotov%2Fts_smoothing_visualizer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdbolotov%2Fts_smoothing_visualizer/lists"}