MegazordNet：结合统计和机器学习观点进行时间序列预测
MegazordNet: combining statistical and machine learning standpoints for time series forecasting
摘要：
由于金融时间序列的混沌特性，对其进行预测一直被认为是一项困难的任务。统计方法在预测市场方向和股票单一价格等一些具体问题上显示出了坚实的结果;然而，随着深度学习和大数据技术的最新进展，出现了新的有希望的选择来解决金融时间序列预测。此外，最近的文献表明，与单一解决方案相比，采用统计和机器学习的组合可以提高预测的准确性。考虑到上述方面，在这项工作中，我们提出了MegazordNet，这是一个结合结构化深度学习模型进行时间序列预测的金融序列中探索统计特征的框架。我们使用不同的指标评估了我们预测标准普尔500指数股票收盘价的方法，我们能够击败单一的统计和机器学习方法。
英文摘要：
Forecasting financial time series is considered to be a difficult task due to the chaotic feature of the series. Statistical approaches have shown solid results in some specific problems such as predicting market direction and single-price of stocks; however, with the recent advances in deep learning and big data techniques, new promising options have arises to tackle financial time series forecasting. Moreover, recent literature has shown that employing a combination of statistics and machine learning may improve accuracy in the forecasts in comparison to single solutions. Taking into consideration the mentioned aspects, in this work, we proposed the MegazordNet, a framework that explores statistical features within a financial series combined with a structured deep learning model for time series forecasting. We evaluated our approach predicting the closing price of stocks in the S&P 500 using different metrics, and we were able to beat single statistical and machine learning methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
