THIS IS A WIP DRAFT FOR THE MULTIVERSE DATASETS AND LEADERBOARD. You can find the archive datasets on Zenodo here: https://zenodo.org/communities/tsml/records?q=&f=subject%3Auea%20archive&l=list&p=1&s=20&sort=newest

Welcome to the Multiverse

The Multiverse archive for multivariate time series classification.

The Multiverse is an expanded archive for multivariate time series classification (MTSC), together with supporting code, metadata, and benchmark results. It consolidates datasets from the original UEA MTSC archive, newer MTSC collections, donated standalone datasets, and associated benchmark results into a single open repository.

The current paper version describes:

• 133 unique MTSC problems
• 147 released datasets when preprocessing variants are included
• a curated 66 dataset subset, Multiverse-core (MV-core), for algorithm benchmarking

This repository aims to make it easier to:

• load Multiverse datasets through aeon
• inspect archive metadata and dataset variants
• reproduce baseline benchmark results
• compare against published and recreated results
• contribute new results, metadata, and documentation as the archive evolves

Datasets · Results · Leaderboard · Evaluation · Classifiers · Contributing

Installation

You can install from pip,

git clone https://github.com/aeon-toolkit/multiverse.git
cd multiverse
pip install -e .

but at present, the best route is to install from source, since it is changing rapidly.

This repository depends on aeon and uses the aeon dataset loading interface as the main public API for archive access.

Quick start

At present, the safest route is to install from source.

git clone https://github.com/aeon-toolkit/multiverse.git
cd multiverse
pip install -e .

This repository depends on aeon and uses the aeon dataset loading interface as the main public API for archive access.

Quick start

Install the release package from PyPI:

pip install aeon-multiverse

or install the development version from GitHub:

pip install git+https://github.com/aeon-toolkit/multiverse.git

Load a dataset

Use aeon to download data from zenodo and load into memory.

from aeon.datasets import load_classification

X, y = load_classification("BasicMotions")
train_X, train_y = load_classification("BasicMotions", split="train")
test_X, test_y = load_classification("BasicMotions", split="test")

print(X.shape)

More info and links to code - docs/datasets.md

Train and test a classifier

from aeon.classification.deep_learning import InceptionTimeClassifier
from multiverse.classification import TimesNet

clf = InceptionTimeClassifier()
clf.fit(X, y)
preds = clf.predict(X)

More info and links to aeon classifiers - docs/classifiers.md Multiverse ported classifiers - multiverse/classification

Compare your results to published results

Load results directly in code

from aeon.classification.deep_learning import InceptionTimeClassifier

Or explore published results explored in this repo - docs/results.md

Run an experiment

To reproduce a benchmark run or evaluate a new classifier, start from:

• experiments/run_single_dataset.py
• experiments/run_benchmark.py

Donate your code and published results

Coming soon

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Repository layout

multiverse/
├── docs/                  # Documentation
├── experiments/           # Benchmark and reproduction scripts
├── results/               # Submitted results and schema
└── multiverse/            # Python package source for classifiers