论文标题
预测：一个时间变化的深层馈送前馈神经网络体系结构，用于多步预测预测
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting
论文作者
论文摘要
经常性和卷积神经网络是深度学习文献中预测时间序列的最常见架构。这些网络通过在时间或空间上重复一组具有固定参数的固定架构来使用参数共享。结果是整体体系结构是时间不变（在空间域中的转移不变）或静止。我们认为，时间传播可以降低执行多步骤预测的能力，在这种情况下，需要在一系列尺度和分辨率下对动态进行建模。我们提出了使用深层馈送架构来提供时间变化模型的预测网。预测网的额外新颖性是交错的输出，我们表明有助于减轻消失的梯度。证明了预测网络在几个数据集上的表现优于统计和深度学习基准模型。
Recurrent and convolutional neural networks are the most common architectures used for time series forecasting in deep learning literature. These networks use parameter sharing by repeating a set of fixed architectures with fixed parameters over time or space. The result is that the overall architecture is time-invariant (shift-invariant in the spatial domain) or stationary. We argue that time-invariance can reduce the capacity to perform multi-step-ahead forecasting, where modelling the dynamics at a range of scales and resolutions is required. We propose ForecastNet which uses a deep feed-forward architecture to provide a time-variant model. An additional novelty of ForecastNet is interleaved outputs, which we show assist in mitigating vanishing gradients. ForecastNet is demonstrated to outperform statistical and deep learning benchmark models on several datasets.
