Soft-dtw: a differentiable loss function for time-series
We propose in this paper a differentiable learning loss between time series, building upon
the celebrated dynamic time warping (DTW) discrepancy. Unlike the Euclidean distance …
the celebrated dynamic time warping (DTW) discrepancy. Unlike the Euclidean distance …
Transfer learning for time series classification
Transfer learning for deep neural networks is the process of first training a base network on
a source dataset, and then transferring the learned features (the network's weights) to a …
a source dataset, and then transferring the learned features (the network's weights) to a …
k-shape: Efficient and accurate clustering of time series
J Paparrizos, L Gravano - Proceedings of the 2015 ACM SIGMOD …, 2015 - dl.acm.org
The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …
substantial interest in the analysis and mining of time series. Clustering is one of the most …
Proximity forest: an effective and scalable distance-based classifier for time series
Research into the classification of time series has made enormous progress in the last
decade. The UCR time series archive has played a significant role in challenging and …
decade. The UCR time series archive has played a significant role in challenging and …
Fuzzy clustering of time series data using dynamic time warping distance
Clustering is a powerful vehicle to reveal and visualize structure of data. When dealing with
time series, selecting a suitable measure to evaluate the similarities/dissimilarities within the …
time series, selecting a suitable measure to evaluate the similarities/dissimilarities within the …
Fast and accurate time-series clustering
J Paparrizos, L Gravano - ACM Transactions on Database Systems …, 2017 - dl.acm.org
The proliferation and ubiquity of temporal data across many disciplines has generated
substantial interest in the analysis and mining of time series. Clustering is one of the most …
substantial interest in the analysis and mining of time series. Clustering is one of the most …
Generating synthetic time series to augment sparse datasets
In machine learning, data augmentation is the process of creating synthetic examples in
order to augment a dataset used to learn a model. One motivation for data augmentation is …
order to augment a dataset used to learn a model. One motivation for data augmentation is …
Dynamic time warping averaging of time series allows faster and more accurate classification
Recent years have seen significant progress in improving both the efficiency and
effectiveness of time series classification. However, because the best solution is typically the …
effectiveness of time series classification. However, because the best solution is typically the …
Dtwnet: a dynamic time warping network
Abstract Dynamic Time Warping (DTW) is widely used as a similarity measure in various
domains. Due to its invariance against warping in the time axis, DTW provides more …
domains. Due to its invariance against warping in the time axis, DTW provides more …
A fine-tuning based approach for daily activity recognition between smart homes
Y Yu, K Tang, Y Liu - Applied Sciences, 2023 - mdpi.com
Daily activity recognition between different smart home environments faces some
challenges, such as an insufficient amount of data and differences in data distribution …
challenges, such as an insufficient amount of data and differences in data distribution …