CASCADED GLOBAL–LOCAL REPRESENTATION LEARNING FOR FINANCIAL TIME-SERIES FORECASTING
Forecasting financial indices remains difficult because market observations combine persistent movements, short-lived disturbances, nonlinear interactions, and substantial noise. A single recurrent architecture may retain nearby temporal information yet fail to represent widely separated dependencies, whereas an attention-based encoder is effective at relating distant positions but does not by itself guarantee detailed sensitivity to local sequence dynamics. This paper reformulates the forecasting pipeline as a cascaded global–local learning problem. A Transformer encoder first converts normalized price windows into contextual representations through positional encoding, multi-head self-attention, residual normalization, and a feedforward sublayer. Those representations are then processed by a bidirectional long short-term memory network, so forward and reverse recurrent states refine the global context before a dense prediction head produces the output. The design was assessed on daily closing-price series for the S&P 500, Dow Jones Industrial Average, and Nasdaq Composite over 2 September 2003–13 July 2023. Preprocessing included interpolation of missing observations, interquartile-range screening of outliers, first differencing, min–max scaling, and windowed sample construction. Tests against recurrent, bidirectional recurrent, feedforward, Informer, and temporal-convolution baselines show that the hybrid system delivers the strongest overall error and goodness-of-fit profile across the three markets. The findings indicate that passing attention-derived context into a bidirectional memory module offers a practical means of combining long-horizon structure with local temporal variation, although computational cost remains relevant for latency-sensitive trading applications.