发表机构
Azad University; University of Isfahan; Iran University of Science and Technology（阿扎德大学; 伊斯法罕大学; 伊朗科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CryptoL提出统一框架，通过上下文归一化、通道依赖变换、尺度自适应稳定化和软可行性损失，缓解金融多元时间序列预测中的尺度主导与OHLC约束违反问题。
AI 中文摘要
加密货币预测呈现出跨资产尺度极端异质性、非平稳动态以及开盘价、最高价、最低价和收盘价（OHLC）变量之间结构性依赖的独特组合。我们提出CryptoL，一个旨在应对多元时间序列预测中这些挑战的统一框架。CryptoL在RevIN流程内的上下文归一化坐标中评估预测误差，防止逆归一化在MSE目标中引入额外的平方尺度权重。我们通过经验风险和参数梯度几何正式刻画了这一效应，建立了大规模资产可能不成比例影响共享模型优化的条件。除损失空间归一化外，CryptoL还考察了针对OHLC数据的通道独立与通道依赖归一化，表明共享的通道依赖仿射变换能保留独立通道变换未必能保留的蜡烛图顺序关系。该框架进一步纳入尺度自适应数值稳定化，以减少跨多个数量级资产中固定归一化常数引起的失真，并辅以软可行性损失，惩罚违反OHLC定义不等式的行为。在异构加密货币资产上的实验通过受控消融评估了这些组件，并展示了相对于所考虑基线在预测准确性、训练稳定性以及财务有效OHLC预测频率方面的改进。因此，CryptoL提供了一种集尺度平衡优化、结构保持归一化、数值稳定化和约束感知加密货币预测于一体的综合方法。
英文摘要
Cryptocurrency forecasting presents a distinctive combination of extreme cross-asset scale heterogeneity, non-stationary dynamics, and structural dependencies among Open, High, Low, and Close (OHLC) variables. We present CryptoL, a unified framework designed to address these challenges within multivariate time-series forecasting. CryptoL evaluates forecasting error in context-normalized coordinates within the RevIN pipeline, preventing inverse normalization from introducing an additional squared-scale weighting into the MSE objective. We formally characterize this effect through the empirical risk and parameter-gradient geometry, establishing the conditions under which large-scale assets can disproportionately influence shared-model optimization. Beyond loss-space normalization, CryptoL examines channel-independent and channel-dependent normalization for OHLC data, showing that a shared channel-dependent affine transformation preserves candle-order relations that independent channel transformations need not preserve. The framework further incorporates scale-adaptive numerical stabilization to reduce distortions caused by a fixed normalization constant across assets spanning many orders of magnitude, together with a soft feasibility loss that penalizes violations of the defining OHLC inequalities. Experiments across heterogeneous cryptocurrency assets evaluate these components through controlled ablations and demonstrate improvements in forecasting accuracy, training stability, and the frequency of financially valid OHLC predictions relative to the considered baselines. CryptoL therefore provides an integrated approach to scale-balanced optimization, structure-preserving normalization, numerical stabilization, and constraint-aware cryptocurrency forecasting.
