Stock market prediction acts as a challenging area for the investors for obtaining the profits in the financial markets. A greater number of models used in stock market forecasting is not capable of providing an accurate prediction. This article proposes a stock market prediction system that effectively predicts the state of the stock market. The deep convolutional long short-term memory (Deep-ConvLSTM) model acts as the prediction module, which is trained by using the proposed Rider-based monarch butterfly optimization (Rider-MBO) algorithm. The proposed Rider-MBO algorithm is the integration of rider optimization algorithm (ROA) and MBO. Initially, the data from the live stock market are subjected to the computation of the technical indicators, representing the features from which the necessary features are obtained through clustering by using the Sparse-Fuzzy C-Means (Sparse-FCM) followed with feature selection. The robust features are given to the Deep-ConvLSTM model to perform an accurate prediction. The evaluation is based on the evaluation metrics, such as mean squared error (MSE) and root mean squared error (RMSE), by using six forms of live stock market data. The proposed stock market prediction model acquired a minimal MSE and RMSE of 7.2487 and 2.6923 that shows the effectiveness of the proposed method in stock market prediction.

中文翻译：

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使用优化的Deep-ConvLSTM模型进行的股市预测。

股市预测是投资者在金融市场上获取利润的挑战领域。股票市场预测中使用的大量模型无法提供准确的预测。本文提出了一种股票市场预测系统，可以有效地预测股票市场的状态。深度卷积长短期记忆（Deep-ConvLSTM）模型充当预测模块，该模型通过使用建议的基于Rider的君主蝴蝶优化（Rider-MBO）算法进行训练。提出的骑手-MBO算法是骑手优化算法（ROA）和MBO的集成。最初，来自牲畜市场的数据要经过技术指标的计算，表示特征，通过使用稀疏-模糊C均值（Sparse-FCM）进行聚类从中获得必要的特征，然后进行特征选择。强大的功能被赋予Deep-ConvLSTM模型以执行准确的预测。通过使用六种形式的实时股票市场数据，评估基于评估指标，例如均方误差（MSE）和均方根误差（RMSE）。所提出的股票市场预测模型获得的最小MSE和RMSE分别为7.2487和2.6923，这表明所提出的方法在股票市场预测中的有效性。例如使用六种形式的实时股票市场数据来表示均方误差（MSE）和均方根误差（RMSE）。所提出的股票市场预测模型获得的最小MSE和RMSE分别为7.2487和2.6923，这表明所提出的方法在股票市场预测中的有效性。例如使用六种形式的实时股票市场数据来表示均方误差（MSE）和均方根误差（RMSE）。所提出的股票市场预测模型获得的最小MSE和RMSE分别为7.2487和2.6923，这表明所提出的方法在股票市场预测中的有效性。