The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …

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 …

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 …

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

Recently, there has been a surge of Transformer-based solutions for the long-term time
series forecasting (LTSF) task. Despite the growing performance over the past few years, we …

We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer
with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph …

Recently many deep models have been proposed for multivariate time series (MTS)
forecasting. In particular, Transformer-based models have shown great potential because …

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 …

Transformers have achieved superior performances in many tasks in natural language
processing and computer vision, which also triggered great interest in the time series …

Aiming to build foundation models for time-series forecasting and study their scaling
behavior, we present here our work-in-progress on Lag-Llama, a general-purpose …