A python library for time-series smoothing and outlier detection in a vectorized way.
Overview
Tsmoothie is an innovative Python library designed specifically for time-series smoothing and outlier detection. It stands out due to its vectorized approach, which enhances performance and efficiency, especially when dealing with large datasets. For data scientists and analysts, Tsmoothie provides an essential toolkit for refining time-series data and ensuring accuracy in data analysis and forecasting.
Whether you’re working with financial data, sensor readings, or any other form of time-series information, Tsmoothie simplifies the process of cleaning and smoothing data. This powerful library is crafted for users who require precision and speed, making it a valuable addition to any data handling repertoire.
Features
- Time-Series Smoothing: Offers advanced algorithms for smoothing time-series data to improve clarity and reliability in trends.
- Outlier Detection: Equipped with methods to identify and handle outliers effectively, ensuring that your analysis is based on accurate data.
- Vectorized Operations: Utilizes vectorization to enhance performance, allowing for faster processing of large time-series datasets.
- User-Friendly Interface: Designed with simplicity in mind, making it easier for users to implement smoothing and outlier detection without extensive coding.
- Flexible Integration: Easily integrates with popular libraries such as NumPy and Pandas, facilitating seamless data manipulation.
- Customizable Parameters: Supports a variety of customizable settings, giving users control over the smoothing processes and detection thresholds.
- Robust Documentation: Comes with comprehensive documentation and examples, guiding users through installation, functionality, and use cases.
