In Chaos, a minor divergence between two initial conditions exhibits
exponential amplification over time, leading to far-away outcomes, known as the
butterfly effect. Thus, the distant future is full of uncertainty and hard to
forecast. We introduce Group Reservoir Transformer to predict long-term events
more accurately and robustly by overcoming two challenges in Chaos: (1) the
extensive historical sequences and (2) the sensitivity to initial conditions. A
reservoir is attached to a Transformer to efficiently handle arbitrarily long
historical lengths, with an extension of a group of reservoirs to reduce the
uncertainty due to the initialization variations. Our architecture consistently
outperforms state-of-the-art DNN models in multivariate time series, including
NLinear, Pyformer, Informer, Autoformer, and the baseline Transformer, with an
error reduction of up to -89.43\% in various fields such as ETTh, ETTm, and air
quality, demonstrating that an ensemble of butterfly learning, the prediction
can be improved to a more adequate and certain one, despite of the traveling
time to the unknown future.

通过引入群组储水池变换器，以应对混沌中存在的两个挑战：大量历史序列和对初始条件的敏感性，更准确、更可靠地预测长期事件，我们的架构在多变量时间序列中始终优于最先进的深度神经网络模型，包括 NLinear、Pyformer、Informer、Autoformer 和基准 Transformer，在 ETTh、ETTm 和空气质量等各个领域中减少了高达 89.43％的误差，证明通过蝴蝶效应学习的集合，预测可以更加准确和确定，尽管面对未知的未来。