STRIDE：基于增量动力学与演化的状态转移表示
STRIDE: State-Transition Representation via Increment Dynamics and Evolution
AI总结：
STRIDE通过导数指纹定义状态并学习转移函数，将连续预测转为转移预测，在230个任务中胜率最高达78.5%，并在Aizawa轨迹上显著降低长期预测误差。
AI中文摘要：
我们提出STRIDE（基于增量动力学与演化的状态转移表示），该方法通过导数指纹定义状态，并学习用于状态转移（qpairs）的局部函数，将连续预测重新构建为转移预测。滑动卷积窗口估计局部联合转移频率，其滞后差分形成高维增量轨迹。本征正交分解（POD）给出坐标路径，由带记忆的稀疏动力学联合预测。将预测重组并应用历史锚定反演，恢复未来转移分布；采样状态路径选择局部函数以生成连续预测。三次种子实验将STRIDE与九个基准族中的十四个基线进行比较。五个独立的马尔可夫和隐状态基线覆盖了所有230个评估任务，其中220个为完整全视界对。与这些比较对象相比，在190个多步对上，系统加权晚期能量胜率介于73.6%至78.5%之间；与DLinear相比，在72个配对多步任务中胜率为77.3%。匹配对照检查中间表示。在64个独立初始化的Aizawa轨迹上，与四个匹配对照相比，晚期能量降低约24%至56%，所有四个预设对比均通过Holm校正。这些结果将转移统计预测与连续概率预测联系起来，在匹配的Aizawa研究中获得了显著的长期收益。
英文摘要：
We introduce STRIDE (State-Transition Representation via Increment Dynamics and Evolution), which defines states through derivative fingerprints and learns local functions for state transitions (qpairs), recasting continuous forecasting as transition prediction. Trailing convolution windows estimate local joint-transition frequencies, whose lagged differences form a high-dimensional increment trajectory. Proper orthogonal decomposition (POD) gives coordinate paths, jointly forecast by sparse dynamics with memory. Recombining their forecasts and applying history-anchored inversion recovers future transition distributions; sampled state paths select local functions to generate continuous forecasts. Three-seed experiments compare STRIDE against fourteen baselines across nine benchmark families. Five independent Markov and hidden-state baselines cover all 230 evaluated tasks, with 220 complete whole-horizon pairs. Against these comparators, system-weighted late-Energy win fractions range from 73.6% to 78.5% on 190 multi-step pairs; against DLinear, the fraction is 77.3% on 72 paired multi-step tasks. Matched controls examine the intermediate representation. On 64 independently initialized Aizawa trajectories, late-Energy reductions against four matched controls range from approximately 24% to 56%, with all four prespecified contrasts passing Holm correction. These results connect transition-statistic prediction to continuous probabilistic forecasting, with substantial long-horizon gains in the matched Aizawa study.
