Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …

Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

Pre-training on time series poses a unique challenge due to the potential mismatch between
pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends …

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

Unsupervised/self-supervised time series representation learning is a challenging problem
because of its complex dynamics and sparse annotations. Existing works mainly adopt the …

Forecasting complex time series is ubiquitous and vital in a range of applications but
challenging. Recent advances endeavor to achieve progress by incorporating various deep …

This work summarizes two ways to accomplish Time-Series (TS) tasks in today's Large
Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a …

Transformers have gained popularity in time series forecasting for their ability to capture
long-sequence interactions. However, their memory and compute-intensive requirements …

Self-Supervised Learning (SSL) is a new paradigm for learning discriminative
representations without labeled data, and has reached comparable or even state-of-the-art …

The emergence of self-supervised learning in the field of wearables-based human activity
recognition (HAR) has opened up opportunities to tackle the most pressing challenges in the …