A decision-focused comparison of the leading time-series foundation models for financial forecasting, from multivariate support and covariates to calibration, latency, licensing, and deployment.
Chart screenshots are intuitive but lossy. This practical guide explains when vision-language models help, why structured OHLCV usually wins for forecasting, and how to combine both safely.
A rigorous protocol for testing Chronos, TimesFM, Moirai, and classical baselines on Bitcoin without random splits, optimistic costs, or hidden leakage.
Why a model with excellent prediction metrics may still lose money—and how costs, calibration, position sizing, execution, and risk convert forecasts into portfolio outcomes.
How to combine related financial signals without creating noise or leakage, from economic feature graphs and timestamp alignment to regime-aware evaluation.
A practical guide to quantiles, intervals, distributions, calibration, proper scoring rules, tail risk, and honest forecast visualization for financial decisions.
A production-oriented architecture for trading agents that separates observation, reasoning, forecasting, deterministic risk, execution, monitoring, and human authority.
A forensic guide to the failures behind unrealistic financial machine-learning results, with controls for point-in-time data, split leakage, repeated testing, and live drift.