论文标题
带有编码器神经网络的预测时间序列
Forecasting time series with encoder-decoder neural networks
论文作者
论文摘要
在本文中，我们考虑了高维固定过程，在过去的观测值的压缩版本中产生了新的观察结果。特定的演化是通过编码器码头结构建模的。我们通过编码器造成神经网络估算进化，并在特定的结构和稀疏假设下为预期的预测误差提供了上限。对于绝对规则的混合系数或观察到的过程的功能依赖度量，分别显示了结果。在定量模拟中，我们讨论了在不同模型假设下网络估计器的行为。我们通过一个真实的数据示例来证实我们的理论，在该示例中我们考虑预测温度数据。
In this paper, we consider high-dimensional stationary processes where a new observation is generated from a compressed version of past observations. The specific evolution is modeled by an encoder-decoder structure. We estimate the evolution with an encoder-decoder neural network and give upper bounds for the expected forecast error under specific structural and sparsity assumptions. The results are shown separately for conditions either on the absolutely regular mixing coefficients or the functional dependence measure of the observed process. In a quantitative simulation we discuss the behavior of the network estimator under different model assumptions. We corroborate our theory by a real data example where we consider forecasting temperature data.
