Development of a Transformer-based Framework for Deep Causal Inference and Prospective Forecasting in Financial Time Series

With the increasing density and complexity of information generated by a financial market, conventional time series analysis techniques become less predictive and less effective for causal inference, particularly in an environment that involves high dimensions and nonlinear characteristics. We present a Transformer based framework that integrates causal inference with predictive boosting to conduct financial time series analysis more accurately. The framework presents a temporal causality modeling module, that improve the current Transformer model, and combines long short-term memory (LSTM) networks to facilitate relation between long time stamp series and complex temporal dependency capturing. Moreover, in the self-attention mechanism with attention to different time steps, enhancing the ability to identify key events, it achieves the joint training of causal inference and future trend prediction through multi-task learning. This powerful combination empowers the model with the ability to deliver more accurate forecasting and causal analysis under highly volatile market environment conditions. Through intensive experiments, the proposed framework significantly outperforms existing benchmark models in terms of predicting accuracy over several financial datasets, especially in the aspect of capturing complex causal relationships and market trends.