b-ptiste/dtw-soft

Authors :

• CALLARD Baptiste (MVA)
• DE SENNEVILLE Adhemar (MVA)

Work overview

Dans le cadre du cours Apprentissage pour les séries temporelles du cours de L. OUDRE nous avons étudier le papier "Soft-DTW: a Differentiable Loss Function for Time-Series".

The report delves into Soft-Dynamic Time Warping (Soft-DTW), a differentiable version of Dynamic Time Warping, suitable for gradient-based optimization in machine learning. It involves reimplementation of models, theoretical and practical analysis, and experimentation with datasets like ArrowHead and ECG200. The findings include :

• An optimized PyTorch-compatible Soft-DTW
• Applications in barycenter averaging
• K-Means clustering
• Anomaly detection

The report concludes with the potential and computational challenges of Soft-DTW, suggesting directions for future research.

Simple utilisation

Our code is compatible with any native Pytorch implementation. We over-write the backward for efficiency purposes.

import torch
from tslearn.datasets import UCR_UEA_datasets
from DTWLoss_CUDA import DTWLoss

# load data
ucr = UCR_UEA_datasets()
X_train, y_train, X_test, y_test = ucr.load_dataset("SonyAIBORobotSurface2")

# convert to torch
X_train = torch.from_numpy(X_train).float().requires_grad_(True)
loss = DTWLoss(gamma=0.1)
optimizer = # your optimizer

##############
# your code ##
##############

value = loss(X_train[0].unsqueeze(0), X_train[1].unsqueeze(0))
optimizer.zero_grad()
value.backward()
optimizer.step()

Nice Experiments

Avering times series

K-MEANS

Anomaly detection

Credit

Soft-dtw: a differentiable loss function for time-series by Cuturi, Marco and Blondel, Mathieu in International conference on machine learning