Transformers have achieved great success in many artificial intelligence fields, such as
natural language processing, computer vision, and audio processing. Therefore, it is natural …

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 …

By encoding time series as a string of numerical digits, we can frame time series forecasting
as next-token prediction in text. Developing this approach, we find that large language …

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 …

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 …

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

Long-term time series forecasting is challenging since prediction accuracy tends to
decrease dramatically with the increasing horizon. Although Transformer-based methods …