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arXiv Machine Learning By Yalda Taheri, Mohammad Hassan Heydari, Armon Rasooli, Maryam Amirshahkarami, Mohammad Ebrahim Mahdavi, Hossein Karshenas

CryptoL: Towards Scale Dominance and Physics Constraints Mitigation in Financial Multivariate Time Series Forecasting

Read the original on arXiv Machine Learning →

CryptoL is a unified framework for forecasting cryptocurrency prices that tackles extreme scale differences, non‑stationary dynamics, and inter‑dependencies among OHLC variables. It normalizes forecasting error in context‑normalized coordinates within the RevIN pipeline, uses channel‑dependent affine transformations to preserve candle‑order relations, and adds scale‑adaptive stabilization and a soft feasibility loss to enforce OHLC inequalities. Experiments on diverse crypto assets show that CryptoL improves accuracy, training stability, and the frequency of financially valid predictions compared to baseline methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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