在高静态性数据上的机器和深度学习模型之间的比较.
Domenico Santoro1, Tiziana Ciano2,3, Massimiliano Ferrara4,5
1Department of Economics, Management and Territory, University of Foggia, 71121, Foggia, FG, Italy.
Scientific reports
|August 21, 2024
概括
机器学习算法,如 eXtreme梯度提升 (XGBoost),可以超过深度学习模型的时间序列预测. 与使用长期短期记忆 (RNN-LSTM) 细胞的循环神经网络相比,XGBoost在预测意大利收费站车辆数量方面取得了更高的准确性.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 时间序列分析时间序列分析
背景情况:
- 大数据分析越来越依赖于先进传感器和通信技术的时间序列数据.
- 传统的预测模型往往难以识别复杂的序列特征并实现高预测准确性.
- 在各种领域中,对分析和预测时间序列数据的强大方法的需求至关重要.
研究的目的:
- 调查时间序列特征,并将机器学习算法的性能与深度学习模型进行比较.
- 分析预测2021年意大利收费站车辆数量的具体问题.
- 确定哪些算法为这个特定的时间序列预测任务提供了更高的准确性.
主要方法:
- 利用与2021年意大利收费站车辆计数相关的8766行和6列数据集.
- 应用机器学习算法:支持矢量机器,随机森林和极端梯度增强 (XGBoost).
- 采用深度学习模型:具有长期短期记忆 (RNN-LSTM) 细胞的循环神经网络.
- 基于平均绝对误差 (MAE) 和平均平方误差 (MSE) 的评估模型.
主要成果:
- 与其他机器学习和深度学习模型相比,Extreme Gradient Boosting (XGBoost) 算法展示了优越的预测性能.
- XGBoost实现了较低的MAE和MSE值,这表明预测车辆数量的准确性更高.
- 该研究发现,一个较浅的机器学习模型 (XGBoost) 比更深的RNN-LSTM模型更好地适应时间序列特征.
结论:
- 机器学习算法,特别是XGBoost,对于某些时间序列预测问题,可能比RNN-LSTM等深度学习模型更有效.
- 选择算法应该考虑时间序列数据的特定特征,例如静止性.
- 更简单,更浅的模型可能在深度模型倾向于过度平滑预测的场景中提供更好的适应性和预测准确性.
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