Financial time series forecasting with a hybrid VMD–CSA–BiT framework (opens in new tab)

Financial time series forecasting faces significant challenges due to inherent nonlinearity, non-stationarity, and high levels of noise. To address these issues, this study proposes VMD–CSA–BiT, an integrated framework that combines variational mode decomposition (VMD), convolutional self-attention (CSA), and bidirectional transformers (BiT) to enhance prediction robustness. The methodology first decomposes raw price series into interpretable intrinsic mode functions via VMD. It then employs ...