论文标题
预测的季节性编码器架构
Seasonal Encoder-Decoder Architecture for Forecasting
论文作者
论文摘要
尤其是一般的深度学习（DL），尤其是复发性神经网络（RNN），基于序列的应用程序的成功水平很高。本文与RNN有关时间序列建模和预测。我们提出了一种新颖的RNN体系结构捕获（随机）季节性相关性，同时可以准确地进行多步骤预测。它是由著名的编码器架构（ED）体系结构和乘法季节性自动回归模型的动机。即使在外源输入的存在（或不存在）的情况下，它也包含了多步（多目标）学习。它可以用于单个或多个序列数据。对于多个序列情况，我们还提出了一种新型的贪婪递归程序，以构建（一个或多个）在序列数据较少时跨序列的预测模型。我们通过广泛的实验证明了我们所提出的体系结构的实用性，无论是单个序列还是多个序列方案。
Deep learning (DL) in general and Recurrent neural networks (RNNs) in particular have seen high success levels in sequence based applications. This paper pertains to RNNs for time series modelling and forecasting. We propose a novel RNN architecture capturing (stochastic) seasonal correlations intelligently while capable of accurate multi-step forecasting. It is motivated from the well-known encoder-decoder (ED) architecture and multiplicative seasonal auto-regressive model. It incorporates multi-step (multi-target) learning even in the presence (or absence) of exogenous inputs. It can be employed on single or multiple sequence data. For the multiple sequence case, we also propose a novel greedy recursive procedure to build (one or more) predictive models across sequences when per-sequence data is less. We demonstrate via extensive experiments the utility of our proposed architecture both in single sequence and multiple sequence scenarios.
