在训练机器学习模型过程中,需要使用损失函数(loss function)来更新模型参数。不同的损失函数惩罚的内容不同,会在训练过程中将模型引到不同的方向。而在时间序列预测中,按照不同的惩罚目标选择或设计损失函数,也会影响模型最终的表现能力。欧几里得损失函数(Euclidean loss,亦即 MSE)是常用的损失函数,这里不再赘述。本文...
This repository provides the implementation of Soft-DTW as loss function for batch processing in Keras/Tensorflow models. First, Euclidean distance matrix is calculated for whole batch at once. In the next step, each sample in the batch is traversed sequentially to calculate loss (distance). To ...
内容提示: Soft-DTW: a Differentiable Loss Function for Time-SeriesMarco Cuturi 1 Mathieu Blondel 2AbstractWe propose in this paper a differentiable learningloss between time series, building upon the cel-ebrated dynamic time warping (DTW) discrep-ancy. Unlike the Euclidean distance, DTW can...
A differentiable learning loss; Introduction: supervised learning: learn a mapping that links an input to an output object. output object is a time series. Prediction: two multi-layer perceptrons, the first use Euclidean loss and the second use soft-DTW as a loss function. ---> soft-DTW, ...
Cuturi, M. and Blondel, M., 2017, August. Soft-DTW: a differentiable loss function for time-series. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 894-903). JMLR. org. https://arxiv.org/pdf/1703.01541.pdfarxiv.org/pdf/1703.01541.pdfG. DTW...
We show in this paper that soft-DTW is a differentiable loss function, and that both its value and its gradient can be computed with quadratic time/space complexity (DTW has quadratic time and linear space complexity). We show that our regularization is particularly well suited to average and...
Soft-DTW loss function for Keras/TensorFlow deep-learningneural-networktensorflowkerasloss-functionssoft-dtwdtw-algorithmdtw-distances UpdatedApr 25, 2024 Python Python implementation of soft-DTW. pythonpython-librarydynamic-time-warpingsoft-dtw UpdatedJul 10, 2020 ...
In contrast to the previous successful usage of DTW as a loss function, the proposed framework leverages DTW to obtain a better feature extraction. For the first time, the DTW loss is theoretically analyzed, and a stochastic backpropogation scheme is proposed to improve the accuracy and ...
Blondel, “Soft-DTW: A differentiable loss function for time-series,” in Proc. 34th Int. Conf. Machine Learning, Sydney, Australia, 2017, pp. 894–903.. Google Scholar [19] S. Salvador and P. Chan. Toward accurate dynamic time warping in linear time and space. Intell. Data Anal...
Soft-DTW: a Differentiable Loss Function for Time-Series Mathieu Blondel, Marco Cuturi 5 Mar 2017 552 Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models Nicolas Thome, Vincent Le Guen 19 Sep 2019 374 Time Series Data Augmentation for Neural Networks by Time Warping ...