ysmooth

ysmooth is a Python library designed for smoothing and filtering time series data. It provides a collection of algorithms to reduce noise and reveal underlying trends in data that changes over time. The library aims to offer a user-friendly interface for applying various smoothing techniques without requiring extensive mathematical background.

The primary function of ysmooth is to process sequential data points, such as those found in sensor readings, financial data, or signal processing. It can help in making data more interpretable and suitable for further analysis or visualization. The library supports several common smoothing methods. These include moving averages, which calculate the average of a subset of data points, and exponential smoothing, which gives more weight to recent observations. Additionally, ysmooth may offer more advanced techniques like Savitzky-Golay filters, which are designed to preserve the shape and features of the data while smoothing.

Users can typically specify the window size or smoothing factor, which controls the degree of smoothing. Larger window sizes or higher smoothing factors generally result in a smoother output but may also lead to the loss of finer details or rapid changes in the data. The library is often implemented in Python, making it accessible to a wide range of users within the data science and scientific computing communities. Its goal is to simplify the process of data preprocessing and enhance the quality of time series analysis.