Forecasting stochastic neural network based on financial empirical mode decomposition
作者： Jie Wang , Jun Wang
DOI: 10.1016/J.NEUNET.2017.03.004
关键词:
摘要: In an attempt to improve the forecasting accuracy of stock price fluctuations, a new one-step-ahead model is developed in this paper which combines empirical mode decomposition (EMD) with stochastic time strength neural network (STNN). The EMD processing technique introduced extract all oscillatory modes embedded series, and STNN established for considering weight occurrence historical data. linear regression performs predictive availability proposed model, effectiveness EMDSTNN revealed clearly through comparing predicted results traditional models. Moreover, evaluated method (q-order multiscale complexity invariant distance) applied measure real index show that indeed displays good performance market fluctuations. A hybrid by combining network.The efficiency fluctuations improved model.The are more accurate than compared models.A distance analysis confirm predicting results.
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