Results used in past bake offs are available on [tsc.com] (https://timeseriesclassification.com) and obtainable in code with aeon.
from aeon.benchmarking.results_loaders import get_available_estimators
cls = get_available_estimators("Classification") # doctest: +SKIP
from aeon.benchmarking.results_loaders import get_estimator_results
cls = ["HC2"] # doctest: +SKIP
data = ["Chinatown", "Adiac"] # doctest: +SKIP
get_estimator_results(estimators=cls, datasets=data) # doctest: +SKIPWe currently store the multiverse results in the results directory. Currently only have accuracy for the default splits for subsets of the multiverse. This is still a work in progress. You will soone be able to explore and download these results interactively on the[multiverse website](COMING SOON).
The dataset lists are
from aeon.datasets.tsc_datasets import multiverse_core, multiverse2026, eeg2026
print(len(multiverse_core)) # 66
print(len(multiverse2026)) # 133
print(len(eeg2026)) # 28The full multiverse has 133 datasets in it. We have results for 17 classifiers on some subset of these problems.
from pathlib import Path
import pandas as pd
# Run this from the repository root
df = pd.read_csv(Path("results") / "multiverse" / "accuracy_mean.csv")
print(df.head())We specify a subset of 66 datasets for evaluation. These are more balanced in application, remove overly similar, too simple or zero information datasets and have a good distribution in size and length.
df = pd.read_csv(Path("results") / "multiverse_core" / "accuracy_mean.csv")
print(df.shape)The EEG archive is a sub-project meant to benchmark EEG classification algorithms. The project is based around aeon-neuro
df = pd.read_csv(Path("results") / "eeg" / "accuracy_mean.csv")
print(df.shape)