时间序列预测的模型选择方法:将谷歌趋势数据纳入澳大利亚宏观指标中
Ali Abdul Karim1, Eric Pardede1, Scott Mann1
1Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
谷歌趋势数据有助于预测澳大利亚的失业率和游客数量. 与传统方法相比,机器学习和深度学习模型在结合搜索趋势时显示出更高的准确性.
科学领域:
- 经济学 经济学 经济学
- 数据科学数据科学数据科学
- 计算社会科学 计算社会科学
背景情况:
- 准确预测宏观经济指标对于经济政策和规划至关重要.
- 传统的时间序列模型可能无法完全捕捉影响经济变量的复杂现实世界的动态.
研究的目的:
- 调查谷歌趋势数据对预测澳大利亚宏观经济指标的有用性.
- 为了比较传统 (SARIMA),机器学习 (SVR) 和深度学习 (CNN) 模型的预测性能.
- 评估预测时间和数据驱动的特征选择对模型准确性的影响.
主要方法:
- 利用谷歌趋势数据与澳大利亚的历史宏观经济数据一起.
- 实施并比较季节性自回归集成移动平均 (SARIMA),支向量回归 (SVR) 和卷积神经网络 (CNN) 模型.
- 采用多步骤方法来评估各种视野的样本外预测绩效.
主要成果:
- 结合来自谷歌趋势的互联网搜索行为数据,可显著提高失业率和访客数量的预测准确度.
- 谷歌趋势数据的有效性因预测时间和采用的特定建模技术而异.
- 该研究确定了最佳的模型和特征组合,以准确地进行宏观经济预测.
结论:
- 谷歌趋势数据为增强宏观经济预测提供了宝贵的补充来源.
- 预测技术 (SARIMA,SVR,CNN) 和预测地平线的选择对于最大限度地提高准确性至关重要.
- 这项研究为选择经济指标高效预测模型提供了数据驱动的框架.
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