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PathFM is a trajectory-based forecasting model that uses flow matching to generate complete future paths for multivariate time series. It distills the sampling process to one or two steps, sharing a history attention cache across draws, allowing multiple path generation in roughly the time of a single quantile query. The model achieves competitive marginal accuracy on fev-bench and TIME, and outperforms baselines on the new Forecast Path Score Bench (fps‑bench) with energy, variogram, and downstream metrics, while maintaining long‑run variability in chaotic systems.
GateTS introduces a streamlined univariate forecasting model that merges sparse Mixture-of-Experts computation with an attention-inspired gating mechanism, eliminating the need for auxiliary load‑balancing losses. The architecture achieves superior predictive accuracy across both short‑ and long‑term horizons, including intermittent signals, while using far fewer parameters than leading transformer models such as PatchTST. Empirical tests on diverse datasets show GateTS is computationally more efficient than LSTM, enabling cost‑effective inference for real‑world time‑series applications.
arXiv:2610.07834v1 Announce Type: new Abstract: Retrieval-augmented time-series forecasting uses the continuations of historical segments similar to the current context as references for a forecaster...
arXiv:2607. 00197v1 Announce Type: new Abstract: Long-horizon multivariate time series forecasting (LTSF) remains challenging due to non-stationarity, regime shifts, and error accumulation.
PathFM is a trajectory-based forecasting model that uses flow matching to generate complete future paths for multivariate time series. By distilling the model to one or two steps and sharing a history attention cache, it can produce multiple paths in the time of a single quantile query and optimize calibration loss directly. The paper demonstrates competitive marginal accuracy on fev-bench and TIME, and superior aggregate path scores on the new fps-bench, while maintaining long‑run variability in chaotic systems.
Timer‑M1 is a pretrained multivariate time‑series foundation model that learns with primitives for zero‑shot forecasting. It uses a primitive‑based data synthesis and pretraining pipeline to generate series with shared temporal and relational patterns, assembling them into multivariate samples with distinct channel roles. The model employs gated two‑dimensional Transformer blocks that dynamically allocate cross‑variate attention, achieving top performance on several large‑scale forecasting benchmarks.