Deep Learning provides useful insights by analyzing information especially in the field of finance with advanced computing technology. Although, RNN–LSTM network with the advantage of sequential learning has achieved great success in the past for time series prediction. Conversely, developing and selecting the best computational optimized RNN–LSTM network for intra-day stock market forecasting is a real challenging task as a researcher. Since it analyses the most volatile data, requires to cope with two big factors such as time lag and the large number of architectural hyperparameters that affect the learning of the model. Furthermore, in addition to the design of this network, several former studies use trial and error based heuristic to estimate these factors which may not guarantee the most optimal network. This paper defines the solution to solve the above-mentioned challenging problems using the hybrid mechanism of the RNN–LSTM network integrating with a metaheuristic optimization technique. For this, a two-hybrid approach namely RNN–LSTM with flower pollination algorithm and RNN–LSTM with particle swarm optimization has been introduced to develop an optimal deep learning model to enhance the intra-day stock market prediction. This model suggests a systematic method which helps us with an automatic generation of optimized network. As the obtained network with tuned hyper parametric values-led towards a more precise learning process with the minimized error rate and accuracy enhancement. In addition, the comparative results evaluated over six different stock exchange datasets reflect the efficacy of an optimized RNN–LSTM network by attaining maximum forecasting accuracy approximately increment of 4–6% using the metaheuristic approach.

中文翻译：

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在 RNN-LSTM 网络上使用元启发式优化增强日内股票市场预测

深度学习通过使用先进的计算技术分析信息，尤其是金融领域的信息，提供有用的见解。尽管如此，具有序列学习优势的RNN-LSTM网络过去在时间序列预测方面取得了巨大成功。相反，作为研究人员，开发和选择最佳计算优化 RNN-LSTM 网络用于日内股票市场预测是一项真正具有挑战性的任务。由于它分析最不稳定的数据，因此需要处理两个大因素，例如时间滞后和影响模型学习的大量架构超参数。此外，除了该网络的设计之外，之前的一些研究使用基于试错法的启发式方法来估计这些可能无法保证最佳网络的因素。本文使用 RNN-LSTM 网络的混合机制与元启发式优化技术相结合，定义了解决上述具有挑战性的问题的解决方案。为此，引入了两种混合方法，即带有花授粉算法的 RNN-LSTM 和带有粒子群优化的 RNN-LSTM，以开发最佳深度学习模型以增强日内股市预测。该模型提出了一种系统方法，可帮助我们自动生成优化网络。由于获得的具有调整超参数值的网络导致了更精确的学习过程，同时最小化了错误率和准确性。此外，