{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"}],"enrichment":{"capability":"Fills missing values in time-series data while respecting irregular time intervals, using spline-based interpolation methods and providing gap analysis tools.","skillfed_tags":["time-series","data-imputation","iot-sensors"],"use_cases":["Impute sensor readings from IoT devices that report at irregular intervals or have communication failures.","Fill gaps in financial time-series data (stock prices, exchange rates) while preserving trend direction.","Analyze and visualize missing data patterns in multi-sensor industrial equipment before preprocessing for ML.","Prepare time-series data for scikit-learn pipelines by handling missing values with time-aware methods.","Reconstruct monotonic or smooth trends in environmental monitoring data (temperature, humidity, pressure)."],"what_it_does":"Time-aware-imputer is a Python library that fills missing values in time-series data by understanding that gaps of different durations require different treatment. Unlike generic imputation methods that ignore temporal structure, this library uses spline interpolation (linear, cubic, quadratic, PCHIP, or Akima) to reconstruct missing values while preserving temporal trends. It wraps scipy's interpolation functions with a scikit-learn compatible API, so it integrates into standard ML pipelines.\n\nThe package provides two main tools: SplineImputer for filling missing values with your choice of interpolation method, and GapAnalyzer for diagnosing missing data patterns\u2014counting gaps, measuring their duration, and visualizing them. It's designed for IoT sensor networks, industrial monitoring, and other scenarios where time-series data arrives irregularly and you need to understand and fill the gaps before analysis or modeling.","worth_installing":"Yes, if you work with irregular time-series data and need intelligent gap-filling. The library is actively maintained, has no known vulnerabilities, and integrates cleanly with pandas and scikit-learn. However, it is early-stage (Alpha, 2 stars, 167 days old), so expect the API or behavior to evolve; use it for production work only if you can tolerate potential breaking changes or have capacity to adapt."},"id":"time-aware-imputer","links":{"html":"https://skillfed.io/packages/time-aware-imputer","md":"https://skillfed.io/packages/time-aware-imputer.md","pypi":"https://pypi.org/project/time-aware-imputer/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-28","license_spdx":null,"license_treatment":"permissive","name":"time-aware-imputer","python_support":"supports_current","summary":"Time-aware missing data imputation for irregular time series"},"popularity":{"monthly_downloads":345368,"position":7364,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.0"}