Time series analysis is of immense importance in extensive applications, such as weather
forecasting, anomaly detection, and action recognition. This paper focuses on temporal …

Although we have witnessed great success of pre-trained models in natural language
processing (NLP) and computer vision (CV), limited progress has been made for general …

Transformers have shown great power in time series forecasting due to their global-range
modeling ability. However, their performance can degenerate terribly on non-stationary real …

Time series forecasting holds significant importance in many real-world dynamic systems
and has been extensively studied. Unlike natural language process (NLP) and computer …

Recent progress in neural forecasting accelerated improvements in the performance of large-
scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. Two …

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 …

As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …

The past decade has witnessed significant advances in time series modeling with deep
learning. While achieving state-of-the-art results, the best-performing architectures vary …

Automatic weather stations are essential for fine-grained weather forecasting; they can be
built almost anywhere around the world and are much cheaper than radars and satellites …

Multivariate time series forecasting focuses on predicting future values based on historical
context. State-of-the-art sequence-to-sequence models rely on neural attention between …