SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting [PDF]
arXiv.org, 2023 RNN-based methods have faced challenges in the Long-term Time Series Forecasting (LTSF) domain when dealing with excessively long look-back windows and forecast horizons.
Shengsheng Lin +5 more
semanticscholar +1 more source
PETformer: Long-Term Time Series Forecasting via Placeholder-Enhanced Transformer [PDF]
IEEE Transactions on Emerging Topics in Computational Intelligence, 2023 Recently, the superiority of Transformer for long-term time series forecasting (LTSF) tasks has been challenged, particularly since recent work has shown that simple models can outperform numerous Transformer-based approaches. This evidence suggests that
Shengsheng Lin +4 more
semanticscholar +1 more source
Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [PDF]
IEEE International Conference on Data Engineering, 2023 Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention ...
Yan Li +6 more
semanticscholar +1 more source
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers [PDF]
International Conference on Learning Representations, 2022 We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are ...
Yuqi Nie +3 more
semanticscholar +1 more source
Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting [PDF]
arXiv.org, 2023 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.
Jiaxin Gao, Wenbo Hu, Yuntian Chen
semanticscholar +1 more source
Short-term time-restricted feeding is safe and feasible in non-obese healthy midlife and older adults. [PDF]
Geroscience, 2020 Martens CR +12 more
europepmc +2 more sources
WFTNet: Exploiting Global and Local Periodicity in Long-Term Time Series Forecasting [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023 Recent CNN and Transformer-based models tried to utilize frequency and periodicity information for long-term time series forecasting. However, most existing work is based on Fourier transform, which cannot capture fine-grained and local frequency ...
Peiyuan Liu +7 more
semanticscholar +1 more source
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting [PDF]
Neural Information Processing Systems, 2022 Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information.
Tian Zhou +6 more
semanticscholar +1 more source
Preformer: Predictive Transformer with Multi-Scale Segment-Wise Correlations for Long-Term Time Series Forecasting [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022 In long-term time series forecasting, most Transformer-based methods adopt the standard point-wise attention mechanism, which not only has high complexity but also cannot explicitly capture the predictive dependencies from contexts since the ...
Dazhao Du, Bing Su, Zhewei Wei
semanticscholar +1 more source
Frontiers in Psychology, 2013 Across cultures, people use spatial representations for time: graphs, time-lines, clocks, sundials, hourglasses, calendars, etc. In language, time is also closely tied to space, with spatial terms often used to describe the order and duration of events. In English, we move the meeting forward, push deadlines back, attend a long concert or go on a short
Asifa eMajid +4 more
openaire +6 more sources