Enterprise Cash Flow and Liability Management Forecast Optimization Implemented by Temporal Fusion Transformer

Abstract

To improve the interpretability of enterprise cash-flow and liability-management forecasting, this study applies a Temporal Fusion Transformer model incorporating financial rule constraints. Static and dynamic feature embedding and bidirectional LSTM encoding are first used to extract long-term evolutionary representations of enterprise financial status. A Gated Residual Network then screens key variables, and prior financial rules, including “debt ratio > 70%” and “ cash flow < 3 months of operating expenses,” are embedded into the multi-head attention layer. A learnable gain coefficient adjusts attention weights for high-risk historical periods, thereby improving the financial rationality and traceability of model decisions. A sequence-to-sequence decoding structure is finally used to jointly forecast multi-period cash-flow and liability indicators. Experimental results show that the proposed method achieves strong predictive accuracy, with an average cash-flow MAPE of 6.3%, an average debt-structure Theil’s U of 0.20, and an average rulecompliance rate of 89.7%. The model can be integrated into enterprise financial decision-support systems to support dynamic debt management, liquidity-risk monitoring, and explainable engineering-oriented financial forecasting.