用专家混合层增强N-BEATS，更好处理不同时间序列的预测。
N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting
- 引入门控专家网络动态分配模型权重，适应不同序列特征。
- 在12个数据集上实现稳定提升，尤其在异构序列上效果显著。
- 通过门控机制揭示哪个专家最适合特定序列，增强可解释性。
深度学习方法在时间序列预测中日益重要。N-BEATS等基于多层感知机堆叠的模型已在基准数据集和竞赛中达到领先性能，且相比其他深度学习方法更具可解释性，能将预测分解为趋势、季节性等成分。本文提出N-BEATS-MOE，基于混合专家（MoE）层对N-BEATS进行扩展。该模型通过门控网络实现动态块权重分配，使模型更适应各时间序列的特性。我们还假设门控机制能通过识别每条序列最相关的专家，提供额外的可解释性。在12个基准数据集上的实验表明，该方法在多个数据集上实现一致改进，尤其在包含异构时间序列的数据集上表现突出。
原文摘要 · Abstract (English)
Deep learning approaches are increasingly relevant for time series forecasting tasks. Methods such as N-BEATS, which is built on stacks of multilayer perceptrons (MLPs) blocks, have achieved state-of-the-art results on benchmark datasets and competitions. N-BEATS is also more interpretable relative to other deep learning approaches, as it decomposes forecasts into different time series components, such as trend and seasonality. In this work, we present N-BEATS-MOE, an extension of N-BEATS based on a Mixture-of-Experts (MoE) layer. N-BEATS-MOE employs a dynamic block weighting strategy based on a gating network which allows the model to better adapt to the characteristics of each time series. We also hypothesize that the gating mechanism provides additional interpretability by identifying which expert is most relevant for each series. We evaluate our method across 12 benchmark datasets against several approaches, achieving consistent improvements on several datasets, especially those composed of heterogeneous time series.
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