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Life sciences · Preprint
arXiv · September 10, 2026
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CryptoL is a proposed framework for multivariate cryptocurrency time-series forecasting that addresses scale heterogeneity and structural constraints through normalization and loss-function modifications. The study reports improvements in forecasting accuracy, training stability, and constraint satisfaction in controlled ablations, but is a preprint without peer review, lacks quantified effect sizes, and does not demonstrate real-world financial impact.
Methodological framework with controlled ablation experiments. Multivariate OHLC time series from heterogeneous cryptocurrency assets with cross-asset scale heterogeneity and non-stationary dynamics.. Intervention: CryptoL framework: context-normalized RevIN pipeline, channel-dependent affine transformation for OHLC data, scale-adaptive numerical stabilization, and soft feasibility loss penalizing OHLC inequality violations.. Compared with: Baseline methods (unspecified in abstract); component ablations of the CryptoL framework..
CryptoL prevents inverse normalization from introducing squared-scale weighting into MSE objective via context-normalized coordinate evaluation. Channel-dependent affine transformation preserves candle-order OHLC relations; independent channel transformations do not. Framework demonstrates improvements in forecasting accuracy, training stability, and frequency of financially valid OHLC predictions relative to baselines.
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A single-arm methodological framework study with controlled ablations on cryptocurrency forecasting, lacking head-to-head RCT comparison, peer review, and clinical or financial hard-endpoint validation.
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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.
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