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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 architec}
a high prediction capacity of the model, which is the ability to capture precise long-range dependency coupling between output and input efficiently. Recent studies have shown the potential of Transformer to increase the prediction capacity. However, there are several severe issues with Transformer
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