Machine Learning with Time Series in Julia
Author alan-turing-institute
19 Stars
Updated Last
2 Years Ago
Started In
May 2020


An MLJ compatible Julia toolbox for machine learning with time series.

Build Status Coverage


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)

# make predictions
y_pred = predict_mode(mach, matrix(X_train))


To find out more, check out our:

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 and Markus Löning.

Active maintainers: