利用物联网进行机器学习和金融大数据控制

Pub Date : 2023-08-28 DOI:10.3233/idt-230156
Jian Xiao
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引用次数: 0

摘要

机器学习算法已广泛应用于金融数据风险预测管理系统中。财务风险的预警和控制是企业投资决策的重要领域,可以有效降低投资风险,保证企业的稳定发展。随着物联网的发展,企业的财务信息是通过企业财务系统中的各种智能设备获取的。在高科技信息时代,大数据为经济社会提供高质量的服务。然而,金融数据量大、复杂、多变,因此对金融数据的分析难度巨大,而随着机器学习算法的深入应用,其不足也逐渐暴露出来。为此,本文收集了某上市集团2005 - 2020年的财务数据,并对数据进行预处理和Feature选择,包括剔除缺失值、Outlier和不相关项。接下来,将这些数据分为训练集和测试集,其中训练集数据用于模型训练,测试集数据用于评估模型的性能。采用基于机器学习算法、基于深度学习网络和本文提出的基于人工智能和大数据技术的模型三种方法构建和比较数据控制模型。在风险事件预测比较方面,本文选取准确率和均方误差两个指标来衡量模型的性能。准确性反映了模型的预测能力,即所有正确预测的样本占总样本量的比例。均方误差用来评价模型的精度和误差,即预测值与真实值之间的平均绝对偏差的平方。本文将三种方法的预测结果与实际值进行了比较,得到了它们的精度和均方误差。实验结果表明,本文提出的基于人工智能和大数据技术的模型比其他两种模型具有更高的精度和更小的均方误差,在风险事件预测中可以达到90%的准确率,证明其具有更高的金融数据风险控制能力。
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Machine learning and financial big data control using IoT
Machine learning algorithms have been widely used in risk prediction management systems for financial data. Early warning and control of financial risks are important areas of corporate investment decision-making, which can effectively reduce investment risks and ensure companies’ stable development. With the development of the Internet of Things, enterprises’ financial information is obtained through various intelligent devices in the enterprise financial system. Big data provides high-quality services for the economy and society in the high-tech era of information. However, the amount of financial data is large, complex and variable, so the analysis of financial data has huge difficulties, and with the in-depth application of machine learning algorithms, its shortcomings are gradually exposed. To this end, this paper collects the financial data of a listed group from 2005 to 2020, and conducts data preprocessing and Feature selection, including removing missing values, Outlier and unrelated items. Next, these data are divided into a training set and a testing set, where the training set data is used for model training and the testing set data is used to evaluate the performance of the model. Three methods are used to build and compare data control models, which are based on machine learning algorithm, based on deep learning network and the model based on artificial intelligence and Big data technology proposed in this paper. In terms of risk event prediction comparison, this paper selects two indicators to measure the performance of the model: accuracy and Mean squared error (MSE). Accuracy reflects the predictive ability of the model, which is the proportion of all correctly predicted samples to the total sample size. Mean squared error is used to evaluate the accuracy and error of the model, that is, the square of the Average absolute deviation between the predicted value and the true value. In this paper, the prediction results of the three methods are compared with the actual values, and their accuracy and Mean squared error are obtained and compared. The experimental results show that the model based on artificial intelligence and Big data technology proposed in this paper has higher accuracy and smaller Mean squared error than the other two models, and can achieve 90% accuracy in risk event prediction, which proves that it has higher ability in controlling financial data risk.
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