Support Vector Machines (SVM) has been a naval research field in scientific research for forecasting. This study deals with the application of SVM in financial time series predicting. This paper suggests a model of stock market prediction based on SVMs with appropriate parameter values. A data set of daily closing prices of five selected companies such as Alhaj Textiles Limited, Apex Tannery Limited, Jamuna Bank Limited, Padma Oil Company, and Square Pharmaceuticals Limited of the Dhaka Stock Exchange (DSE) from 01 January 2017 to 13 August 2019 was selected and uses these data to train the model and checks the predictive power of the model. The obtained results show that all the companies closing stock prices are non-stationary. Also the number of support vectors and mean square error is decreasing pattern with the increase of kernel parameter. It is also found that original data and predicted data are very much identical. The result shows that in all the cases SVM model has some predictive power it can be used to forecast financial time series. Several methods, such as SVM, ARIMA, single exponential smoothing, and double exponential smoothing, were performed to predict Bangladesh's stock market. Amazingly, the outcome shows the most efficient method to be Support Vector Machine because of its lowest forecasting errors.
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机译：支持向量机（SVM）一直是对预测科研的海军研究领域。本研究涉及SVM在金融时序序列预测中的应用。本文建议基于SVMS具有适当参数值的股票市场预测模型。 2017年1月01日至2019年8月1日从2019年1月1日至2019年8月1日起，达卡证券交易所（Jamuna Bank Limited），达卡证券交易所（DSE），达卡证券交易所（DSE），Jamuna Bank Limited，Jamuna Bank Limited，Padma石油公司和Square Pharmaceutical Limited等五个选定公司的数据集。选择并使用这些数据培训模型并检查模型的预测电源。获得的结果表明，关闭股票价格的所有公司都是非静止的。随着核心参数的增加，支持向量和均方误差的数量和均方误差正在减小模式。还发现原始数据和预测数据非常相同。结果表明，在所有情况下，SVM模型都有一些预测电源，可用于预测财务时间序列。进行了几种方法，例如SVM，Arima，单指数平滑和双指数平滑，以预测孟加拉国的股市。令人惊讶的是，结果显示了最有效的方法，因为其预测误差最低。
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