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.
A task-based guide to financial LLMs covering filings, news, sentiment, multimodal reports, retrieval, numerical tools, privacy, and model governance.
A practical examination of Chronos-2, including univariate and multivariate tasks, covariates, in-context learning, uncertainty, financial evaluation, and production deployment.
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.