An MLJ compatible Julia toolbox for machine learning with time series.
To install MLJTime.jl, launch Julia and run:
]add "https://github.com/alan-turing-institute/MLJTime.jl.git"MLJTime.jl requires Julia version 1.0 or greater.
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))To find out more, check out our:
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.
- If you are interested, please raise an issue or get in touch with the MLJTime team on slack.
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 and Markus Löning.
Active maintainers:
- Aadesh Deshmukh
- Markus Löning
- Sebastian Vollmer