Transformer-based models have achieved some success in time series forecasting. Existing
methods mainly model time series from limited or fixed scales, making it challenging to …

Recent studies have demonstrated the great power of Transformer models for time series
forecasting. One of the key elements that lead to the transformer's success is the channel …

The performance of time series forecasting has recently been greatly improved by the
introduction of transformers. In this paper, we propose a general multi-scale framework that …

Long-term time series forecasting has received a lot of popularity because of its great
practicality. It is also an extremely challenging task since it requires using limited …

The recent boom of linear forecasting models questions the ongoing passion for
architectural modifications of Transformer-based forecasters. These forecasters leverage …

Long-term time series forecasting (LTSF) is a crucial aspect of modern society, playing a
pivotal role in facilitating long-term planning and developing early warning systems. While …

Long-term time series forecasting (LTSF) provides substantial benefits for numerous real-
world applications, whereas places essential demands on the model capacity to capture …

Many real-world applications require precise and fast time-series forecasting. Recent trends
in time-series forecasting models are shifting from LSTM-based models to Transformer …

The success of Transformers in long time series forecasting (LTSF) can be attributed to their
attention mechanisms and non-autoregressive (NAR) decoder structures, which capture …

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 …