LatentTSF improves time series forecasting accuracy and representation quality by shifting prediction from observation space to a learned latent state space via autoencoding.
Patch-wise structural loss for time series forecasting.arXiv preprint arXiv:2503.00877
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
fields
cs.LG 6representative citing papers
A topological evaluation framework for transformer time series forecasts that derives fidelity scores from persistence diagrams of delay embeddings, including a phase-aware localized metric.
STaT is a Symbolic-Temporal-Textual Alignment model that integrates three modalities to reduce shape distortion in non-stationary time series forecasting, reporting up to 8.9% gains in magnitude metrics and 8.5% less distortion on eight benchmarks.
ReNF proposes Boosted Direct Output (BDO) and parameter smoothing so a basic temporal MLP outperforms complex state-of-the-art models on long-term time series forecasting benchmarks by implicitly combining forecasts to reduce uncertainty.
A linear-complexity architecture with balanced square partitioning and hierarchical low-rank linear interactions outperforms attention/graph baselines on four large-scale traffic forecasting datasets.
VLBM learns a shared latent basis for stable ID dynamics and orthogonal OOD residuals via variational alignment of future-aware posterior with future-blind prior, reporting 15.08% MAE and 7.74% MSE gains on 12 OOD benchmarks.
citing papers explorer
-
From Observations to States: Latent Time Series Forecasting
LatentTSF improves time series forecasting accuracy and representation quality by shifting prediction from observation space to a learned latent state space via autoencoding.
-
TopoCast: A Topological Fidelity Framework for Evaluating Transformer-Based Time Series Forecasting
A topological evaluation framework for transformer time series forecasts that derives fidelity scores from persistence diagrams of delay embeddings, including a phase-aware localized metric.
-
STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy
STaT is a Symbolic-Temporal-Textual Alignment model that integrates three modalities to reduce shape distortion in non-stationary time series forecasting, reporting up to 8.9% gains in magnitude metrics and 8.5% less distortion on eight benchmarks.
-
ReNF: Rethinking the Design of Neural Long-Term Time Series Forecasters
ReNF proposes Boosted Direct Output (BDO) and parameter smoothing so a basic temporal MLP outperforms complex state-of-the-art models on long-term time series forecasting benchmarks by implicitly combining forecasts to reduce uncertainty.
-
SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting
A linear-complexity architecture with balanced square partitioning and hierarchical low-rank linear interactions outperforms attention/graph baselines on four large-scale traffic forecasting datasets.
-
VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting
VLBM learns a shared latent basis for stable ID dynamics and orthogonal OOD residuals via variational alignment of future-aware posterior with future-blind prior, reporting 15.08% MAE and 7.74% MSE gains on 12 OOD benchmarks.