In this paper, a new machine learning (ML) technique is proposed that uses the fine-tuned version of support vector regression for stock forecasting of time series data. Grid search technique is applied over training dataset to select the best kernel function and to optimize its parameters. The optimized parameters are validated through validation dataset. Thus, the tuning of this parameters to their optimized value not only increases model’s overall accuracy but also requires less time and memory. Further, this also minimizes the model from being data overfitted. The proposed method is used to analysis different performance parameters of stock market like up-to-daily and up-to-monthly return, cumulative monthly return, its volatility nature and the risk associated with it. Eight different large-sized datasets are chosen from different domain, and stock is predicted for each case by using the proposed method. A comparison is carried out among the proposed method and some similar methods of same interest in terms of computed root mean square error and the mean absolute percentage error. The comparison reveals the proposed method to be more accurate in predicting the stocks for the chosen datasets. Further, the proposed method requires much less time than its counterpart methods.

本文提出了一种新的机器学习（ML）技术，该技术使用支持向量回归的微调版本对时间序列数据进行股票预测。网格搜索技术应用于训练数据集，以选择最佳内核函数并优化其参数。优化参数通过验证数据集进行验证。因此，将此参数调整为最佳值不仅可以提高模型的整体精度，而且需要更少的时间和内存。此外，这还使模型不会因数据过拟合而最小化。该方法用于分析股票市场的不同绩效参数，如每日和每月收益，累计每月收益，波动性以及与之相关的风险。从不同的领域中选择了八个不同的大型数据集，并通过使用所提出的方法来预测每种情况下的库存。在计算出的均方根误差和平均绝对百分比误差方面，对所提出的方法和一些同样感兴趣的类似方法进行了比较。比较结果表明，所提出的方法在预测所选数据集的存量方面更为准确。此外，所提出的方法比其对应方法所需的时间少得多。