# sryza/spark-timeseries

A library for time series analysis on Apache Spark

Repository: https://github.com/sryza/spark-timeseries
Canonical: https://ross.abutalabs.com/products/spark-timeseries
Language: Scala
License: Apache-2.0
License Family: permissive
Last push: 2020-10-13T04:36:27+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4193, "days_push": 2150, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1196, forks 414 (observed 2026-08-28T04:03:57.287150+00:00)

## What it is
A Scala/Java/Python library for time series analysis on Apache Spark, providing distributed abstractions for large time series datasets plus statistical models and tests. It is no longer under active development, though pull requests are still reviewed.

## Use cases
- analyze large time series datasets on spark
- distributed time series forecasting
- manipulate time series data at scale like pandas
- fit statistical time series models on spark
- work with time series in scala or python on spark

## When to choose
- you need pandas-like time series operations on massive datasets in Spark
- you want statistical time series models (ARIMA-style) running distributed on a Spark cluster
- you're already invested in the JVM/Spark ecosystem and need time series abstractions

## When to avoid
- you need actively maintained software with bug fixes and new features
- your time series data fits in memory on a single machine - use pandas or statsmodels instead
- you want modern Spark DataFrame-native time series tooling

## Facets
- artifact type: library
- maturity: maintenance
- function: data-science, machine-learning, math
- domain: time-series, big-data, data-science
- platform: jvm, python
- tags: time-series-analysis, apache-spark, distributed-computing, scala, forecasting, spark

## Member repositories
- sryza/spark-timeseries (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:57.287150+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:22:04.389988+00:00, confidence not recorded.
  - readme: https://github.com/sryza/spark-timeseries (fetched 2026-08-28T04:03:57.287150+00:00, sha 83a2931240cb)
- Data as of 2026-08-30T08:39:29.467469+00:00.