This is the source code of our paper "LITE: Light Inception with boosTing tEchniques for Time Series Classification" accepted at the 10th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2023) in the Learning from Temporal Data (LearnTeD) special session track.
This work was done by Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber and Germain Forestier.
The same LITE architecture is then used to form an ensemble of five LITE models, names LITETime.
To use this code, running the main.py file with the following options in the parsed as arguments:
--dataset : to choose the dataset from the UCR Archive (default=Coffee)
--classifier : to choose the classifier, in this case only LITE can be chosen (default=LITE)
--runs : to choose the number of runs (default=5)
--output-directory : to choose the output directory name (default=results/)
--track-emissions : to choose wether or not to track the training/testing time and CO2/power consumotion.
The only change to be done in the code is the folder_path in the utils/utils.py file, this line. This directory should point to the parent directory of the UCR Archive datasets.
Using aeon-toolkit to Train a LITETimeClassifier on your DATA
When using aeon, simply load your data and create an instance of a LITETime classifier and train it
from aeon.datasets import load_classification
from aeon.classification.deep_learning import LITETimeClassifier
xtrain, ytrain, _ = load_classification(name="Coffee", split="train")
xtest, ytest, _ = load_classification(name="Coffee", split="test")
clf = LITETimeClassifier(n_classifiers=5)
clf.fit(xtrain, ytrain)
print(clf.score(xtest, ytest)
Results can be found in the results.csv file for FCN, ResNet, Inception, InceptionTime, ROCKET, MultiROCKET, LITE and LITETime. For non-ensemble methods, results are averaged over five runs, for ensemble methods, we ensemble the five runs.
The following figure shows the comparison between LITE and state of the art complex deep learners. The comparison consists on the average performance and the number FLOPS.
The following compares LITE with FCN, ResNet and Inception using the accuracy performance on the test set of the 128 datasts of the UCR archive.
The following compares LITE with FCN, ResNet and Inception using the accuracy performance on the test set of the 128 datasts of the UCR archive.
The following 1v1 and multi-comparison matrix shows the performance of LITETime with respect to the SOTA models for Time Series Classification.
The following compares LITE with FCN, ResNet and Inception using the accuracy performance on the test set of the 128 datasts of the UCR archive.
The following Critical Difference Diagram (CDD) shows the comparison following the average rank between classifiers.
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@inproceedings{Ismail-Fawaz2023LITELightInception,
author = {Ismail-Fawaz, A. and Devanne, M. and Berretti, S. and Weber, J. and Forestier, G.},
title = {LITE: Light Inception with boosTing tEchniques for Time Series Classification},
booktitle = {International Conference on Data Science and Advanced Analytics (DSAA)},
year = {2023}
}
This work was supported by the ANR DELEGATION project (grant ANR-21-CE23-0014) of the French Agence Nationale de la Recherche. The authors would like to acknowledge the High Performance Computing Center of the University of Strasbourg for supporting this work by providing scientific support and access to computing resources. Part of the computing resources were funded by the Equipex Equip@Meso project (Programme Investissements d’Avenir) and the CPER Alsacalcul/Big Data. The authors would also like to thank the creators and providers of the UCR Archive.
