https://github.com/everval/longmemory.jl
Julia package to generate, estimate, and forecast long memory processes
https://github.com/everval/longmemory.jl
arfima cross-sectional-aggregation forecasting fractional-differencing har-model long-memory long-range-dependence stochastic-duration-shock strong-persistence time-series time-series-analysis
Last synced: 9 months ago
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Julia package to generate, estimate, and forecast long memory processes
- Host: GitHub
- URL: https://github.com/everval/longmemory.jl
- Owner: everval
- License: mit
- Created: 2023-09-28T12:35:29.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2025-09-19T13:08:52.000Z (10 months ago)
- Last Synced: 2025-10-21T12:55:08.274Z (9 months ago)
- Topics: arfima, cross-sectional-aggregation, forecasting, fractional-differencing, har-model, long-memory, long-range-dependence, stochastic-duration-shock, strong-persistence, time-series, time-series-analysis
- Language: Julia
- Homepage: https://everval.github.io/LongMemory.jl/
- Size: 1.76 MB
- Stars: 12
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.bib
Awesome Lists containing this project
README
# LongMemory
[](https://everval.github.io/LongMemory.jl/)
[](https://everval.github.io/LongMemory.jl/dev/)
[](https://github.com/everval/LongMemory.jl/actions/workflows/CI.yml?query=branch%3Amaster)
[](https://codecov.io/gh/everval/LongMemory.jl)
[](https://doi.org/10.5281/zenodo.15096772)
[](https://doi.org/10.21105/joss.07708)
## About
**LongMemory.jl** is a package for time series long memory modelling in [***Julia***](https://julialang.org/).
The package provides functions for *generating long memory*, *estimating the parameters of the models*, and *forecasting*.
Generating methods include *fractional differencing*, *stochastic error duration*, and *cross-sectional aggregation*.
Estimators include *classic* ones used to estimate the Hurst effect, those inspired by the *log-periodogram regression*, and *parametric* ones.
Forecasting is provided for all parametric estimators.
Moreover, the package adds *plotting capabilities* to illustrate long memory dynamics and forecasting.
Finally, the package includes the *Nile River minima* and *Northern Hemisphere Temperature Anomalies* data sets to illustrate the use of the functions.
## Installation
The package is registered in the Julia General registry and can be installed with the Julia package manager.
From the Julia REPL, type `]` to enter the Pkg REPL mode and run:
```julia
pkg> add LongMemory
```
Or, equivalently, via the `Pkg` API:
```julia
julia> using Pkg; Pkg.add("LongMemory")
```
## Usage
Once installed, the package can be imported with the command:
```julia
julia> using LongMemory
```
## Documentation
The package documentation is available [here](https://everval.github.io/LongMemory.jl/) or the link below.
## Examples
An illustrative example of the package usage can be found [here.](https://everval.github.io/files/LM_notebook_illustration.html)
## Benchmarks
The following [notebook](https://everval.github.io/files/LM_notebook_benchmark.html) contains benchmarks for some of the functions in the package against popular ***R*** packages: ***fracdiff*** and ***longMemoryTS***.
## Citation
If you use this package in your research, please cite it as:
Vera-Valdés, J. E., (2025). LongMemory.jl: Generating, Estimating, and Forecasting Long Memory Models in Julia. Journal of Open Source Software, 10(108), 7708, [https://doi.org/10.21105/joss.07708](https://doi.org/10.21105/joss.07708)
```bibtex
@article{Vera-Valdés2025,
author = {J. Eduardo Vera-Valdés},
title = {LongMemory.jl: Generating, Estimating, and Forecasting Long Memory Models in Julia},
journal = {Journal of Open Source Software},
doi = {10.21105/joss.07708},
url = {https://doi.org/10.21105/joss.07708},
year = {2025},
publisher = {The Open Journal},
volume = {10},
number = {108},
pages = {7708}
}
```
## Contributing
All types of contributions are encouraged and appreciated.
If you find a bug or have a feature request, please open a new [issue](https://github.com/everval/LongMemory.jl/issues). If you would like to contribute code, please open a [pull request](https://github.com/everval/LongMemory.jl/pulls). I welcome all contributions, including bug fixes, documentation improvements, and new features.
Thank you for considering contributing!