HypLTSF introduces a hyperbolic geometric framework for long‑term time series forecasting by embedding multi‑scale representations into the Poincaré ball. The approach imposes radial and angular constraints to align the geometry with temporal hierarchies, ensuring that coarser scales abstract finer ones and that related fine‑scale patterns share common ancestors. Experiments on forecasting benchmarks demonstrate that this explicit geometric modeling of the hierarchy yields state‑of‑the‑art performance.
arXiv:2609.08286v1 Announce Type: new Abstract: Multi-scale modeling has become an effective approach for long-term time series forecasting, capturing temporal patterns that range from fine-grained l...
arXiv:2610.15760v1 Announce Type: cross Abstract: Multiscale structure is central to time series forecasting: temporal signals contain slowly varying trends, intermediate seasonal patterns, and high-...
arXiv:2608. 08675v1 Announce Type: new Abstract: Long-term time series forecasting benefits from preserving global structure such as trends and seasonality.
The paper introduces a graph deep learning framework that learns to forecast spatiotemporal data across multiple hierarchical structures, using variations among hierarchies as multi‑task regularization. By jointly training on different aggregation levels, the model reduces over‑specialization and improves forecasting accuracy. A novel top‑down operator ensures coherent forecasts with linear time and space complexity, making the approach computationally efficient.
arXiv:2606. 28553v1 Announce Type: new Abstract: In many real-world applications, such as retail sales, energy usage, and supply chain planning, forecasting is performed across hierarchical structures.
LagNet introduces LagLayer, a Toeplitz‑structured operator that ties history‑to‑future connections by relative lag, replacing traditional temporal linear layers in forecasting models. By stacking LagLayers with activations and multiple feature maps, LagNet achieves competitive performance on long‑term time series benchmarks. Sensitivity matrix diagnostics and limited‑data experiments show that LagLayer offers a sample‑efficient alternative to linear layers.
arXiv:2603. 02220v2 Announce Type: replace-cross Abstract: Time series forecasting remains a challenging problem due to the intricate entanglement of intra-period fluctuations and inter-period trends.
arXiv:2607. 01918v1 Announce Type: new Abstract: We present Zeus, a unified tuning-free Time Series Foundation Model (TSFM) that delivers superior performance across diverse analysis tasks without any task-specific fine-tuning.
arXiv:2508. 02753v5 Announce Type: replace-cross Abstract: Time Series Forecasting (TSF) faces persistent challenges in modeling intricate temporal dependencies across different scales.
The paper introduces Horizon-Resolved eXplanation (HRX), a framework that adds a horizon axis to time‑series forecasting explanations, allowing each forecast step to have its own importance map. HRX operates as a plug‑in for any differentiable forecaster, includes an evaluation protocol that tests the impact of removing top‑ranked inputs, and a rank criterion to decide when horizon resolution is beneficial. Experiments across multiple backbones and datasets demonstrate that incorporating the horizon axis improves explanation quality and that the step‑wise dependence is low‑dimensional, requiring only a few shared maps regardless of forecast length.
arXiv:2609.36119v1 Announce Type: new Abstract: Spatial-temporal (ST) forecasting underpins many real-world systems such as traffic, climate, and energy networks. While existing methods implicitly as...
NVExplain is a model‑agnostic framework that explains time‑series forecasting by attributing each forecast horizon to temporally relevant historical lags. It models forecasting as a latent trajectory, introduces semantic flow to track information evolution, and aggregates this into a lag‑horizon attribution matrix. The method also generates structure‑preserving perturbations and fits sparse local surrogates to produce human‑readable, temporally coherent explanations, and demonstrates competitive faithfulness and stability across benchmark datasets.