https://github.com/alan-turing-institute/timeseriesclassification.jl
Machine Learning with Time Series in Julia
https://github.com/alan-turing-institute/timeseriesclassification.jl
julia machine-learning time-series
Last synced: about 1 year ago
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Machine Learning with Time Series in Julia
- Host: GitHub
- URL: https://github.com/alan-turing-institute/timeseriesclassification.jl
- Owner: alan-turing-institute
- License: mit
- Created: 2020-05-06T16:20:21.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-12-01T15:36:57.000Z (over 5 years ago)
- Last Synced: 2025-04-09T01:51:22.850Z (about 1 year ago)
- Topics: julia, machine-learning, time-series
- Language: Julia
- Homepage:
- Size: 1.14 MB
- Stars: 27
- Watchers: 7
- Forks: 5
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# MLJTime
An [MLJ](https://github.com/alan-turing-institute/MLJ.jl) compatible Julia toolbox for machine learning with
time series.
[](https://github.com/alan-turing-institute/MLJTime.jl/actions)
[](http://codecov.io/github/alan-turing-institute/MLJTime.jl?branch=master)
## Installation
To install MLJTime.jl, launch Julia and run:
```julia
]add "https://github.com/alan-turing-institute/MLJTime.jl.git"
```
MLJTime.jl requires Julia version 1.0 or greater.
## Quickstart
```julia
using MLJTime
# load data
X, y = ts_dataset("Chinatown")
# split data into training and test set
train, test = partition(eachindex(y), 0.7, shuffle=true, rng=1234) #70:30 split
X_train, y_train = X[train], y[train];
X_test, y_test = X[test], y[test];
# train model
model = TimeSeriesForestClassifier(n_trees=3)
mach = machine(model, matrix(X_train), y_train)
fit!(mach)
# make predictions
y_pred = predict_mode(mach, matrix(X_train))
```
## Documentation
To find out more, check out our:
* [Blog post](https://nextjournal.com/aa25desh)
* [Tutorial](https://github.com/aa25desh/MLJTimeTutorials.jl)
## Future work
In future work, we want to add:
* Support for multivariate time series,
* Shapelet based classification algorithms,
* Enhancements to KNN (KDTree and BallTree algorithms),
* Forecasting framework.
## How contribute
* If you are interested, please raise an issue or get in touch with the MLJTime team on slack.
## About the project
This project was originally developed as part of the Google Summer of Code 2020 with the support of the Julia community and my mentors [Sebastian Vollmer](https://warwick.ac.uk/fac/sci/maths/people/staff/vollmer/) and [Markus Löning](https://github.com/mloning).
Active maintainers:
* [Aadesh Deshmukh](https://github.com/aa25desh)
* Markus Löning
* Sebastian Vollmer