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TimeArrays.jl
Julia time series math, resampling, and missing-value fills
What it is
A Julia type for irregular time series, with arithmetic, sliding-window smoothers, resampling, and fills for missing ticks. Addition and similar operations line values up by timestamp rather than by position, and gaps become missing where the other series has no observation. Simple, weighted, and exponential moving averages are included, custom element types can be resampled into candlesticks, and tables can move to and from DataFrames or CSV through Tables.jl. Julia researchers who already hold their own ticks are the audience: the package does not download market history, and the price series in the examples are local samples.
At a glance
Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Developers |
|---|---|
| Used for | Data analysis, Strategy research |
| Markets | Multi-market |
| Stack | Julia |
| Learning curve | Moderate learning curve |
| Practical value | Medium practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| Maintenance | Last commit 4 months ago |
GitHub stars, last 30 days
Daily snapshots since 2026-09-28 (up to 30 days): +1 over the period, now 41. Gaps mean no snapshot was taken that day.
Time series handling for Julia.
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