A review on distance based time series classification
Time series classification is an increasing research topic due to the vast amount of time
series data that is being created over a wide variety of fields. The particularity of the data …
series data that is being created over a wide variety of fields. The particularity of the data …
Ts2vec: Towards universal representation of time series
This paper presents TS2Vec, a universal framework for learning representations of time
series in an arbitrary semantic level. Unlike existing methods, TS2Vec performs contrastive …
series in an arbitrary semantic level. Unlike existing methods, TS2Vec performs contrastive …
Activity recognition with evolving data streams: A review
Activity recognition aims to provide accurate and opportune information on people's
activities by leveraging sensory data available in today's sensory rich environments …
activities by leveraging sensory data available in today's sensory rich environments …
Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion
Unsupervised/self-supervised time series representation learning is a challenging problem
because of its complex dynamics and sparse annotations. Existing works mainly adopt the …
because of its complex dynamics and sparse annotations. Existing works mainly adopt the …
Using dynamic time war** distances as features for improved time series classification
RJ Kate - Data mining and knowledge discovery, 2016 - Springer
Dynamic time war** (DTW) has proven itself to be an exceptionally strong distance
measure for time series. DTW in combination with one-nearest neighbor, one of the simplest …
measure for time series. DTW in combination with one-nearest neighbor, one of the simplest …
DA-Net: Dual-attention network for multivariate time series classification
Time series feature learning with labeled and unlabeled data
Time series classification has attracted much attention in the last two decades. However, in
many real-world applications, the acquisition of sufficient amounts of labeled training data is …
many real-world applications, the acquisition of sufficient amounts of labeled training data is …