AileenNielsen/TimeSeriesAnalysisWithPython resource
None observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3718
- days_rel: n/a
- days_push: 1590
- n_releases_24m: 0
Adoption not part of the score
1923 stars · 1094 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
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
learning-resource · maturity maintenance
data-science math time-series data-science tutorials python time-series-analysis jupyter-notebooks tutorial statistics
1 source
- readme: https://github.com/AileenNielsen/TimeSeriesAnalysisWithPython · fetched 2026-08-28 · 91055dfd9d48
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| AileenNielsen/TimeSeriesAnalysisWithPython | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="AileenNielsen/TimeSeriesAnalysisWithPython")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem