A Multivariate Financial Time Series Foundation Model with Test-Time Learning Relational Attention

Multivariate forecasting can improve financial forecast accuracy by drawing on related time series that provide predictive information beyond the target series’ own history. However, financial time series are noisy, cross-series predictive relationships are non-stationary, and datasets contain different combinations of heterogeneous variables. We introduce FTFM, a financial time-series foundation model that couples causal temporal attention with Test-Time Learning Relational Attention. Causal temporal attention encodes each series’ history. Relational attention then combines a slow path that weights these features by learned compatibility with a fast path guided by a directed predictive relation memory. Feature compatibility can remain high even as predictive relationships weaken. The relation memory adds direct feedback on how well temporal features across series predict subsequent observed features, using the resulting prediction errors to revise the relationships guiding cross-series attention during inference. Furthermore, similar normalized trajectories can represent different meanings and forecasting implications, we introduce semantic embeddings that preserve distinctions between series such as prices, returns, and volatility after normalization, while shared projections and sampling-interval conditioning support different series combinations and temporal resolutions. In empirical experiments across eight financial, economic, and synthetic domains, FTFM achieves the lowest forecasting errors among the evaluated models on the majority of benchmark tasks, both before and after LoRA adaptation.