Transcription of Informer: Beyond Efficient Transformer for Long Sequence ...
{{id}} {{{paragraph}}}
Example: bachelor of science
Informer: Beyond Efficient Transformer for Long Sequence ...
Informer: Beyond Efficient Transformer for Long SequenceTime- series ForecastingHaoyi Zhou,1 Shanghang Zhang,2 Jieqi Peng,1 Shuai Zhang,1 Jianxin Li,1 Hui Xiong,3 Wancai Zhang41 Beihang University2UC Berkeley3 Rutgers University4 SEDD Company{zhouhy, pengjq, zhangs, real-world applications require the prediction of longsequence time- series , such as electricity consumption plan-ning. Long Sequence time- series forecasting (LSTF) demandsa high prediction capacity of the model, which is the abilityto capture precise long-range dependency coupling betweenoutput and input efficiently. Recent studies have shown thepotential of Transformer to increase the prediction , there are several severe issues with Transformerthat prevent it from being directly applicable to LSTF, includ-ing quadratic time complexity, high memory usage, and in-herent limitation of the encoder-decoder architecture. To ad-dress these issues, we design an efficient Transformer -basedmodel for LSTF, named Informer, with three distinctive char-acteristics: (i) aProbSparseself-attention mechanism, whichachievesO(LlogL)in time complexity and memory usage,and has comparable performance on sequences dependencyalignment.}
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting Haoyi Zhou, 1 Shanghang Zhang, 2 Jieqi Peng, 1 Shuai Zhang, 1 Jianxin Li, 1 Hui Xiong, 3 Wancai Zhang 4 1 Beihang University 2 UC Berkeley 3 Rutgers University 4 SEDD Company fzhouhy, pengjq, zhangs, lijxg@act.buaa.edu.cn, shz@eecs.berkeley.edu, …
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: