sryza/spark-timeseries
This system is a time-series analysis library for Apache Spark, providing distributed data structures and statistical modeling capabilities. It enables users to create, manipulate, and analyze time-series data through Java, Scala, and Python APIs, supporting operations like lagging, resampling, and imputation. The library includes implementations for various forecasting models, including ARIMA, GARCH, and EWMA, alongside statistical tests for unit root analysis. It is designed to integrate with Spark's ecosystem, offering serialization optimization and compatibility with Pandas and Java 8 time APIs.
57.1
Adequate · 3 August 2026
8.1k
lines of production code
Scala
with Python
1
bus factor · 29 authors in all
3
measurements over time
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About this page
- The description of this project is derived from its own commit history, not from its README.
- The score is its highest published measurement, taken on 3 August 2026 at a pinned commit. It is not a live figure and does not change until the project is measured again.
- Measured at commit 280aa887dc — the exact code this score is about.
- Scored under rubric rubric-2026.08.18. Score the same commit under that rubric and you get the same number.