Mortality forecasting methods in the Lee-Carter tradition extrapolate temporal components via time-series models, producing forecasts that can systematically underpredict life expectancy at long horizons and require ad hoc adjustments for sex coherence. We reframe forecasting as integrating a flow field through the low-dimensional score space of a Tucker tensor decomposition of multi-population mortality data from the Human Mortality Database. PCA reduction of the effective core matrices reveals that the mortality transition is essentially a one-dimensional flow: a scalar speed function advances the level, trajectory functions supply the structural scores, and the Tucker reconstruction produces complete sex-specific mortality schedules at each horizon. An era-weighted speed function adapts to contemporary dynamics at each forecast origin, and empirically calibrated convergence rates control relaxation from country-specific to canonical mortality structure. The system is evaluated by leave-country-out cross-validation with a 50-year horizon against Lee-Carter and Hyndman-Ullah benchmarks.

翻译：遵循Lee-Carter传统的死亡率预测方法通过时间序列模型外推时间成分，其产生的预测在长期范围内可能系统性低估预期寿命，并需针对性别一致性进行临时调整。我们将预测重新定义为：通过人类死亡率数据库中多群体死亡率数据的Tucker张量分解所获得的低维得分空间中的流场积分。对有效核心矩阵进行主成分分析降维表明，死亡率转型本质上是单维度流场：标量速度函数推进水平值，轨迹函数提供结构得分，而Tucker重构在每个时间跨度生成完整的性别特异性死亡率表。基于时代的加权速度函数在每个预测起点适应当前动态，经验校准的收敛速率控制从国家特异性向规范死亡率结构的松弛过程。该系统通过留国家交叉验证以50年跨度进行评估，并与Lee-Carter及Hyndman-Ullah基准方法进行对比。