A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility information
捕捉潜在波动信息的分解集成时间序列预测框架
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…