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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: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: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.
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.