Ahmad Zafar

Overcoming Context Bottlenecks in Financial Time-Series Forecasting via Dynamic External Memory Augmented LSTMs

This paper introduces the Dynamic External Memory LSTM (DEM-LSTM), a novel deep neural architecture designed to address the hidden state information bottleneck and temporal context decay inherent to standard LSTMs in financial time-series forecasting. By decoupling sequence proce...

Overcoming Context Bottlenecks in Financial Time-Series Forecasting via Dynamic External Memory Augmented LSTMs

This paper introduces the Dynamic External Memory LSTM (DEM-LSTM), a novel deep neural architecture designed to address the hidden state information bottleneck and temporal context decay inherent to standard LSTMs in financial time-series forecasting. By decoupling sequence proce...