Stock index prediction based on wavelet transform and FCD‐MLGRU

Xiaojun Li · Pan Tang

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

Abstract 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.

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References

2018Time series forecasting of petroleum production using deep LSTM recurrent networks 2018How Valuable Is FinTech Innovation?2019To FinTech and Beyond 2018An Ensemble of LSTM Neural Networks for High-Frequency Stock Market Classification 2017Forecasting financial time series volatility using Particle Swarm Optimization trained Quantile Regression Neural Network

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