本文的核心思想如上图,作者希望将时间序列分解为短期变化(short-term variations)和长期变化(long-term variations),然后分别对它们进行建模。短期变化就是每个周期内的波动;长期变化就是多个周期中相同阶段的变化,有点像趋势信息。 具体模型结构可看下图,总的思路就是,先用多周期解耦块(Multi-periodic Decoupling Block...
The time series forecasting problem is to predict the most probable length-O series in the future given the past length-I series, denoting as input-I-predict-O. The long-term forecasting setting is to predict the long-term future given the short-term history, i.e. O≫I. Challenge 原始...
PERIODICITY DECOUPLING FRAMEWORK FOR LONGTERM SERIES FORECASTING 13:08 通用预测-深度数据依赖型近似分析模型 (DAM) 13:30 24年华为研究生数学建模C题赛后唠嗑 16:32 pathformer-自适应多尺度Transformer 21:12 论文汇报之MetaTST-使用元数据结合Transformer做信息预测-thuml组最新工作 21:48 国内时序领域...
原始题目:Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting 中文翻译:Autoformer:用于长期序列预测的自相关分解变压器 发表时间:2021年 平台:Advances in Neural Information Processing Systems 文章链接:https://proceedings.neurips.cc/paper/2021/hash/bcc0d400288793e8bd...
Long-term time series forecasting is a challenging problem both in theory and in practice. Although the idea of information granulation has been shown to be an essential concept and algorithmic pursuit in time series prediction, there is still an acute need for developing a sound conceptual ...
This is a Pytorch implementation of TSCT: "Improving Long-Term Electricity Time Series Forecasting in Smart Grid with a Three-Stage Channel-Temporal Approach". Features Support both Univariate and Multivariate long-term time series forecasting. ...
Long-term time series forecasting (LTSF) aims to predict future values of a time series given the past values. The current state-of-the-art (SOTA) on this problem is attained in some cases by linear-centric models, which primarily feature a linear mapping layer. However, due...
An official implementation of "Periodicity Decoupling Framework for Long-term Series Forecasting" (ICLR 2024) - Hank0626/PDF
Long-term time series forecasting is crucial in various domains, including weather, traffic, and energy. In a time series, the time domain contains intuitive time-varying characteristics, affording valuable insights into predicting trends and details, while the frequency domain harbors underlying ...
(2022). FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. arXiv preprint arXiv:2201.12740. [2] 阿里达摩院最新FEDformer,长程时序预测全面超越SOTA | ICML 2022 [3] GitHub - MAZiqing/FEDformer 编辑于 2023-12-05 10:56・IP 属地广东...