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zero-shot forecasting

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Oct 10, 2026 · Artificial Intelligence

Google TimesFM-3: Native Multivariate Forecasting with Alternating Attention

Google's TimesFM-3 introduces native multivariate time series forecasting with alternating causal temporal and full variate attention, non-autoregressive decoding via contiguous patch masking, and support for historical and future-known covariates, achieving top rankings on Gift-Eval, FEV-Bench, and TIME benchmarks without sacrificing univariate performance.

Google ResearchTimesFM-3alternating attention

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Google TimesFM-3: Native Multivariate Forecasting with Alternating Attention

Bighead's Algorithm Notes

Oct 23, 2025 · Artificial Intelligence

FinCast: A Foundation Model for Financial Time‑Series Forecasting

FinCast introduces a decoder‑only Transformer foundation model for financial time‑series forecasting that tackles non‑stationarity, multi‑domain diversity, and multi‑resolution challenges through input chunking with frequency embeddings, a sparse MoE decoder, and a PQ‑loss, achieving zero‑shot and supervised gains over state‑of‑the‑art baselines while running five times faster on consumer GPUs.

PQ lossSparse MoETransformer

0 likes · 12 min read

FinCast: A Foundation Model for Financial Time‑Series Forecasting