With the development of artificial intelligence, deep learning is widely used in the field of nonlinear time series forecasting. It is proved in practice that deep learning models have higher forecasting accuracy compared with traditional linear econometric models and machine learning models. With the purpose of further improving forecasting accuracy of financial time series, we propose the WT‐FCD‐MLGRU model, which is the combination of wavelet transform, filter cycle decomposition and multilag neural networks. Four major stock indices are chosen to test the forecasting performance among traditional econometric model, machine learning model and deep learning models. According to the result of empirical analysis, deep learning models perform better than traditional econometric model such as autoregressive integrated moving average and improved machine learning model SVR. Besides, our proposed model has the minimum forecasting error in stock index prediction.

中文翻译：

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基于小波变换和FCD-MLGRU的股指预测

随着人工智能的发展，深度学习已广泛应用于非线性时间序列预测领域。实践证明，与传统的线性计量经济学模型和机器学习模型相比，深度学习模型具有更高的预测精度。为了进一步提高金融时间序列的预测准确性，我们提出了WT-FCD-MLGRU模型，该模型是小波变换，滤波周期分解和多时延神经网络的结合。在传统的计量经济学模型，机器学习模型和深度学习模型中，选择了四种主要的股票指数来测试预测性能。根据实证分析的结果，深度学习模型的表现优于传统的计量经济学模型，例如自回归综合移动平均值和改进的机器学习模型SVR。此外，我们提出的模型在股指预测中具有最小的预测误差。