MPSTime.jl

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A Julia package for Matrix-Product State (MPS)-based time-series analysis.

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<h1 align="center"><em>MPSTime.jl</em>: Matrix-Product States for Time-Series Analysis</h1>

__MPSTime__ is a Julia package for learning the joint probability distribution of time series directly from data using matrix-product state (MPS) methods inspired by quantum many-body physics. 
It provides a unified formalism for classifying unseen data, as well as imputing gaps in time-series data, which regularly occur in real-world datasets due to sensor failure, routine maintenance, or other problems.

## Installation
MPSTime can be installed using the Julia package manager:

```julia
julia> ]
pkg> add MPSTime
```

## Usage and Documentation
We provide [tutorials](https://hugopstackhouse.github.io/MPSTime.jl/dev/classification/) and basic usage examples in the documentation:
- [LATEST DOCS](https://hugopstackhouse.github.io/MPSTime.jl/) -- the latest documentation.

## Citation
If you use this software in your work, please read and cite the [published paper](https://journals.aps.org/prresearch/abstract/10.1103/61h8-8qr5):
```
@misc{MPSTime2025,
  title = {Using matrix-product states for time-series machine learning},
  author = {Moore, Joshua B. and Stackhouse, Hugo P. and Fulcher, Ben D. and Mahmoodian, Sahand},
  journal = {Phys. Rev. Res.},
  volume = {7},
  issue = {4},
  pages = {043010},
  numpages = {31},
  year = {2025},
  month = {Oct},
  publisher = {American Physical Society},
  doi = {10.1103/61h8-8qr5},
  url = {https://link.aps.org/doi/10.1103/61h8-8qr5}}
```

## Contributing to MPSTime
We welcome contributions from the community! 
MPSTime is under active development and research, making it an exciting opportunity for collaboration.
Whether you are interested in adding new features, improving existing documentation, fixing bugs, or sharing ideas, your input is valuable.
Feel free to submit a [PR](https://github.com/hugopstackhouse/MPSTime.jl/pulls) or open an [issue](https://github.com/hugopstackhouse/MPSTime.jl/issues) for discussion.

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