# AileenNielsen/TimeSeriesAnalysisWithPython

Repository: https://github.com/AileenNielsen/TimeSeriesAnalysisWithPython
Canonical: https://ross.abutalabs.com/products/timeseriesanalysiswithpython
Language: Jupyter Notebook
License Family: other
Last push: 2022-04-26T13:23:19+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": 3718, "days_push": 1590, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1923, forks 1094 (observed 2026-08-28T04:05:55.304066+00:00)

## What it is
A collection of Jupyter Notebooks accompanying Aileen Nielsen's work on time series analysis with Python. It serves as educational material covering time series methods and techniques.

## Use cases
- learn time series analysis in python
- jupyter notebooks for forecasting
- study time series statistics examples
- understand arima and time series models
- educational material for temporal data analysis

## When to choose
- you want hands-on notebook examples for learning time series concepts
- you are following Aileen Nielsen's time series book or talks
- you prefer code-driven tutorials over documentation

## When to avoid
- you need a production-ready time series library
- you require maintained software with a license and active support
- you need a packaged tool rather than educational notebooks

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, math
- domain: time-series, data-science, tutorials
- platform: python
- tags: time-series-analysis, jupyter-notebooks, tutorial, statistics

## Member repositories
- AileenNielsen/TimeSeriesAnalysisWithPython (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:55.304066+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 2026-08-30T03:09:35.384861+00:00, confidence not recorded.
  - readme: https://github.com/AileenNielsen/TimeSeriesAnalysisWithPython (fetched 2026-08-28T04:05:55.304066+00:00, sha 91055dfd9d48)
- Data as of 2026-08-30T08:39:29.467469+00:00.