论文标题
关于经济预测的错误校正神经网络
On Error Correction Neural Networks for Economic Forecasting
论文作者
论文摘要
复发性神经网络（RNN）更适合从观察到的时间序列数据中学习动态系统中的非线性依赖性。实际上，所有驱动此类系统的外部变量尚不清楚，尤其是在经济预测中。一类称为误差校正神经网络（ECNN）的RNN旨在补偿缺失的输入变量。它通过在当前步骤中反馈到上一个步骤中的错误来做到这一点。 ECNN通过适当梯度的计算在Python中实施，并在股票市场预测中进行了测试。正如预期的那样，它执行了简单的RNN和LSTM以及其他混合模型，该模型涉及降低预处理步骤。后者的直觉是，降价可能导致信息丢失。
Recurrent neural networks (RNNs) are more suitable for learning non-linear dependencies in dynamical systems from observed time series data. In practice all the external variables driving such systems are not known a priori, especially in economical forecasting. A class of RNNs called Error Correction Neural Networks (ECNNs) was designed to compensate for missing input variables. It does this by feeding back in the current step the error made in the previous step. The ECNN is implemented in Python by the computation of the appropriate gradients and it is tested on stock market predictions. As expected it out performed the simple RNN and LSTM and other hybrid models which involve a de-noising pre-processing step. The intuition for the latter is that de-noising may lead to loss of information.
