注意一下代码里面的Channel Independence: Convert Multivariate series to Univariate series的操作即可。 class TiDE(nn.Module): def __init__( self, L, H, r, r_hat, p, hidden_dim, encoder_layer_num, decoder_layer_num, featureProjectionHidden, temporalDecoderHidden, ): super(TiDE, self).__...
Solving Long Sequence time series forecasting(LSTF) is the major problem. Some new models have been developed like transformers that show superior performance in capturing long-range time series data than RNN(recurrent neural networks) models. The transformer takes a lot ofGPUcomputing power, so us...
E. Carreno Jara, Long memory time series forecasting by using genetic programming. Genetic Programming and Evolvable Machines 12 (4), 429–456 (2012)Emiliano Carreo Jara, Long memory time series forecasting by using genetic programming, Genetic Programming and Evolvable Machines, v.12 n.4, p....
本周阅读的论文是题目为《Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting》的一篇2021年发表在AAAI会议上涉及时间序列预测问题的文章。 2、 摘要 在很多实际问题应用中,需要对长时时间序列问题进行预测,例如用电消耗规划。长时时间序列预测(LSTF)要求模型具有较强的预测能力,即能够有...
原始题目:Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting 中文翻译:Informer:超越有效变换器进行长序列时间序列预测 发表时间:2021-05-18 平台:Proceedings of the AAAI Conference on Artificial Intelligence 文章链接:https://ojs.aaai.org/index.php/AAAI/article/view/17325 开源...
Short sequence time-series forecasting no longer satisfies the current research community, and long-term future prediction is becoming the hotspot, which is noted as long sequence time-series forecasting (LSTF). The LSTF has been widely studied in the extant literature, but few reviews of its ...
2021AAAI-BestPaper-Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting 作者以及单位 周号益 北航 解决问题 从论文作者演讲的ppt来看,Transformer存在一些严重的问题:二次时间复杂度、高内存使用率以及encoder-decoder体系结构的固有限制。
(GPR), and Long Short-Term Memory (LSTM) models are developed to predict long-term time series of average monthly reservoir inflows in Baroon Dam (... A Farshad,G Redvan,G Amin - 《Earth Science Informatics》 被引量: 0发表: 2024年 Uncertainty estimation for time series forecasting via ...
或者是:CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns GitHub:https://github.com/ACAT-SCUT/CycleNet NeurIPS 2024 Spotlight的论文。 这篇论文是SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters这篇论文的改进版本。
Credit to Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting [Neurips 2022] Benchmark Result SCINet SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction 这篇文章发表在Neurips2022,代码已开源。 这里初步地汇集一下文章中的结果: 短期时间序列预测...