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Patch-wise Structural Loss for Time Series Forecasting

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arxiv 2503.00877 v2 pith:F6LJLNEW submitted 2025-03-02 cs.LG

Dilfira Kudrat , Zongxia Xie , Yanru Sun , Tianyu Jia , Qinghua Hu This is my paper
classification cs.LG
keywords lossstructuraltimeseriesforecastingpoint-wiseaccuratelycomplex
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Time-series forecasting has gained significant attention in machine learning due to its crucial role in various domains. However, most existing forecasting models rely heavily on point-wise loss functions like Mean Square Error, which treat each time step independently and neglect the structural dependencies inherent in time series data, making it challenging to capture complex temporal patterns accurately. To address these challenges, we propose a novel Patch-wise Structural (PS) loss, designed to enhance structural alignment by comparing time series at the patch level. Through leveraging local statistical properties, such as correlation, variance, and mean, PS loss captures nuanced structural discrepancies overlooked by traditional point-wise losses. Furthermore, it integrates seamlessly with point-wise loss, simultaneously addressing local structural inconsistencies and individual time-step errors. PS loss establishes a novel benchmark for accurately modeling complex time series data and provides a new perspective on time series loss function design. Extensive experiments demonstrate that PS loss significantly improves the performance of state-of-the-art models across diverse real-world datasets.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From Observations to States: Latent Time Series Forecasting

    cs.LG 2026-01 conditional novelty 7.0 of 10

    LatentTSF improves time series forecasting accuracy and representation quality by shifting prediction from observation space to a learned latent state space via autoencoding.

  2. TopoCast: A Topological Fidelity Framework for Evaluating Transformer-Based Time Series Forecasting

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    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.

  3. SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    SqLinear uses geometry-adaptive square partitioning with theoretical guarantees plus hierarchical linear interactions to cut MAE 2.3-5.8% and training time 13-31% on large traffic datasets versus 10 baselines.

  4. STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy

    cs.LG 2026-05 unverdicted novelty 6.0 of 10

    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 ...

  5. Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting

    cs.LG 2026-01 conditional novelty 6.0 of 10

    A model-agnostic module that retrieves common and rare prototype patterns improves forecasting error on many standard benchmarks, but not on all reported cases.

  6. ReNF: Rethinking the Design of Neural Long-Term Time Series Forecasters

    cs.LG 2025-09 conditional novelty 6.0 of 10

    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 t...

  7. Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Framework

    cs.LG 2026-07 conditional novelty 5.0 of 10

    WrapFlow combines continuous-time event/gap tokenization with simulation-free residual flow matching on a Transformer to improve irregular multivariate time-series forecasting.

  8. SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting

    cs.LG 2026-06 conditional novelty 5.0 of 10

    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.

  9. VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting

    cs.LG 2026-06 unverdicted novelty 5.0 of 10

    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.

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