Multi-horizon time series forecasting, crucial across diverse domains,
demands high accuracy and speed. While AutoRegressive (AR) models excel in
short-term predictions, they suffer speed and error issues as the horizon
extends. Non-AutoRegressive (NAR) models suit long-term predictions but
struggle with interdependence, yielding unrealistic results. We introduce
AMLNet, an innovative NAR model that achieves realistic forecasts through an
online Knowledge Distillation (KD) approach. AMLNet harnesses the strengths of
both AR and NAR models by training a deep AR decoder and a deep NAR decoder in
a collaborative manner, serving as ensemble teachers that impart knowledge to a
shallower NAR decoder. This knowledge transfer is facilitated through two key
mechanisms: 1) outcome-driven KD, which dynamically weights the contribution of
KD losses from the teacher models, enabling the shallow NAR decoder to
incorporate the ensemble's diversity; and 2) hint-driven KD, which employs
adversarial training to extract valuable insights from the model's hidden
states for distillation. Extensive experimentation showcases AMLNet's
superiority over conventional AR and NAR models, thereby presenting a promising
avenue for multi-horizon time series forecasting that enhances accuracy and
expedites computation.

AMLNet 是一种创新的非自回归模型，通过在线知识蒸馏方法实现真实的预测，结合了自回归和非自回归模型的优势，并通过两个关键机制实现知识传递。大量的实验表明，AMLNet 优于传统的自回归和非自回归模型，提供了一个提高准确性和加快计算速度的多时间段时间序列预测的有希望途径。

AMLNet：非自回归多时标时间序列预测的对抗求知神经网络

AMLNet: Adversarial Mutual Learning Neural Network for  Non-AutoRegressive Multi-Horizon Time Series Forecasting

In this paper, we introduce ProNet, an novel deep learning approach designed
for multi-horizon time series forecasting, adaptively blending autoregressive
(AR) and non-autoregressive (NAR) strategies. Our method involves dividing the
forecasting horizon into segments, predicting the most crucial steps in each
segment non-autoregressively, and the remaining steps autoregressively. The
segmentation process relies on latent variables, which effectively capture the
significance of individual time steps through variational inference. In
comparison to AR models, ProNet showcases remarkable advantages, requiring
fewer AR iterations, resulting in faster prediction speed, and mitigating error
accumulation. On the other hand, when compared to NAR models, ProNet takes into
account the interdependency of predictions in the output space, leading to
improved forecasting accuracy. Our comprehensive evaluation, encompassing four
large datasets, and an ablation study, demonstrate the effectiveness of ProNet,
highlighting its superior performance in terms of accuracy and prediction
speed, outperforming state-of-the-art AR and NAR forecasting models.

介绍了一种名为 ProNet 的全新深度学习方法，专为多时间段时间序列预测而设计，自适应地融合了自回归（AR）和非自回归（NAR）策略。通过将预测时间段划分为若干段，采用非自回归策略对每一段中最关键的步骤进行预测，而对剩余的步骤采用自回归策略。该方法使用潜在变量进行分段，通过变分推断有效地捕捉了各个时间步骤的重要性。相较于自回归模型，ProNet 具有明显优势，可减少自回归迭代次数，预测速度更快，减少误差累积。与非自回归模型相比，ProNet 考虑了输出空间中预测的相互依赖关系，提高了预测准确性。对四个大型数据集进行的全面评估和消融研究表明，ProNet 具有良好的效果，其在准确性和预测速度上优于最先进的自回归和非自回归预测模型。

ProNet：面向多时间跨度时间序列预测的渐进神经网络

ProNet: Progressive Neural Network for Multi-Horizon Time Series  Forecasting

Encoder-decoder deep neural networks have been increasingly studied for
multi-horizon time series forecasting, especially in real-world applications.
However, to forecast accurately, these sophisticated models typically rely on a
large number of time series examples with substantial history. A rapidly
growing topic of interest is forecasting time series which lack sufficient
historical data -- often referred to as the ``cold start'' problem. In this
paper, we introduce a novel yet simple method to address this problem by
leveraging graph neural networks (GNNs) as a data augmentation for enhancing
the encoder used by such forecasters. These GNN-based features can capture
complex inter-series relationships, and their generation process can be
optimized end-to-end with the forecasting task. We show that our architecture
can use either data-driven or domain knowledge-defined graphs, scaling to
incorporate information from multiple very large graphs with millions of nodes.
In our target application of demand forecasting for a large e-commerce
retailer, we demonstrate on both a small dataset of 100K products and a large
dataset with over 2 million products that our method improves overall
performance over competitive baseline models. More importantly, we show that it
brings substantially more gains to ``cold start'' products such as those newly
launched or recently out-of-stock.

使用图神经网络作为数据增强来改善编码器，在冷启动产品的需求预测中取得明显的收益。