https://github.com/mlefkir/tonari.jl
Time series analysis in Julia
https://github.com/mlefkir/tonari.jl
fourier-analysis julia power-spectrum-estimation time-series time-series-analysis
Last synced: 12 days ago
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Time series analysis in Julia
- Host: GitHub
- URL: https://github.com/mlefkir/tonari.jl
- Owner: mlefkir
- License: mit
- Created: 2024-01-10T19:41:57.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-11-25T17:52:53.000Z (7 months ago)
- Last Synced: 2025-12-12T14:52:44.536Z (6 months ago)
- Topics: fourier-analysis, julia, power-spectrum-estimation, time-series, time-series-analysis
- Language: Julia
- Homepage: https://mlefkir.github.io/Tonari.jl/
- Size: 38.1 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Tonari
[](https://github.com/mlefkir/Tonari.jl/actions/workflows/documentation.yml) [](https://github.com/mlefkir/Tonari.jl/actions/workflows/testbuild.yml) [](https://github.com/JuliaTesting/Aqua.jl)
[](https://codecov.io/gh/mlefkir/Tonari.jl)
Tonari is a Julia package for the analysis of time series with an emphasis on astronomical time series. I am implementing the methods and functions I need along the way, so it is a work in progress.
## Installation
Since it's Julia package, you need... Julia! See the official documentation here: [https://julialang.org/](https://julialang.org/).
To enter Pkg mode by type `]` in the julia REPL
```julia
pkg>
```
To install Tonari type:
```julia
pkg> add Tonari
```
and then load it with:
```julia
using Tonari
```
## Documentation
The documentation is available here: [https://mlefkir.github.io/Tonari.jl/](https://mlefkir.github.io/Tonari.jl/)
## Current features
- Power spectral density models: power-law with zero, one or two bends, Lorentzian...
- Simulation of time series with power spectral density (PSD) models (e.g. bending power law, Lorentzian, etc.)
- Simulation of bivariate time series with a cross-spectral density (CSD) model
- Periodogram computation
- Cross-periodogram for two time series with coherence, phase, and lag
- Interpolation of time series with gaps + randomisation
- Delay estimation with the interpolated cross-correlation function (ICCF)
- Time series structure to represent regularly and irregularly sampled time series
## Ideas/possible features
### for the 0.3.x
- Periodogram fitting with Whittle likelihood, essentially connect sampler and a good likelihood definition.
- Extracting light curves from event lists, mainly from X-ray observations.
### for the 0.4.x and later
- Operation on light curves, concatenate light curves with dates, i.e. making a big light curve.
- Bispectrum computation for fun.