一种新颖的分解-集成时间序列预测框架：捕捉潜在波动率信息
A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility information
AI总结：
本文提出一种结合Variational Mode Decomposition分解与GARCH波动率提取的分解-集成时间序列预测框架，利用神经网络融合数值与波动率信息，显著降低了MSE、RMSE和MAPE。
摘要：
时间序列预测在各个领域都是一项重要且具有挑战性的任务。近年来，基于模态分解的方法因其能够捕捉局部特征并从数据中提取固有模态的优势，在复杂时间序列预测中占据主导地位。然而，大多数模型未能捕捉到包含重要信息的隐含波动率。为了增强对当代多样化和复杂时间序列的预测能力，我们提出了一种新颖的时间序列预测范式，该范式将分解方法与捕捉序列潜在波动信息的能力相结合。在我们的方法中，我们采用Variational Mode Decomposition算法将时间序列分解为K个不同的子模态。分解完成后，我们应用Generalized Autoregressive Conditional Heteroskedasticity（GARCH）模型提取这些子模态中的波动率信息。随后，每个子模态的数值数据和波动率信息都被用于训练一个神经网络。该网络擅长预测子模态的信息，我们汇总所有子模态的预测结果以生成最终输出。通过整合计量经济学和人工智能方法，并同时考虑时间序列的数值信息和波动率信息，我们提出的框架在时间序列预测中展现出优越的性能，对比实验结果中MSE、RMSE和MAPE的显著下降证明了这一点。
英文摘要：
Time series forecasting represents a significant and challenging task across various fields. Recently, methods based on mode decomposition have dominated the forecasting of complex time series because of the advantages of capturing local characteristics and extracting intrinsic modes from data. Unfortunately, most models fail to capture the implied volatilities that contain significant information. To enhance the prediction of contemporary diverse and complex time series, we propose a novel time series forecasting paradigm that integrates decomposition with the capability to capture the underlying fluctuation information of the series. In our methodology, we implement the Variational Mode Decomposition algorithm to decompose the time series into K distinct sub-modes. Following this decomposition, we apply the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to extract the volatility information in these sub-modes. Subsequently, both the numerical data and the volatility information for each sub-mode are harnessed to train a neural network. This network is adept at predicting the information of the sub-modes, and we aggregate the predictions of all sub-modes to generate the final output. By integrating econometric and artificial intelligence methods, and taking into account both the numerical and volatility information of the time series, our proposed framework demonstrates superior performance in time series forecasting, as evidenced by the significant decrease in MSE, RMSE, and MAPE in our comparative experimental results.
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。
