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 …

This paper presents TS2Vec, a universal framework for learning representations of time
series in an arbitrary semantic level. Unlike existing methods, TS2Vec performs contrastive …

Time series constitute a challenging data type for machine learning algorithms, due to their
highly variable lengths and sparse labeling in practice. In this paper, we tackle this …

Enhancing the expressive capacity of deep learning-based time series models with self-
supervised pre-training has become ever-increasingly prevalent in time series classification …

While the celebrated Word2Vec technique yields semantically rich representations for
individual words, there has been relatively less success in extending to generate …

Time-series forecasting is an important problem across a wide range of domains. Designing
accurate and prompt forecasting algorithms is a non-trivial task, as temporal data that arise …

Distance measures are core building blocks in time-series analysis and the subject of active
research for decades. Unfortunately, the most detailed experimental study in this area is …

We study time-series classification (TSC), a fundamental task of time-series data mining.
Prior work has approached TSC from two major directions:(1) similarity-based methods that …

The analysis of time series is becoming increasingly prevalent across scientific disciplines
and industrial applications. The effectiveness and the scalability of time-series mining …

Learning from Multivariate Time Series (MTS) has attracted widespread attention in recent
years. In particular, label shortage is a real challenge for the classification task on MTS …