TL;DR
A phase-aware modeling framework with adaptive frequency decoupling, termed PAFD, for non-stationary time series forecasting that performs adaptive frequency decoupling under time-varying spectral structures to effectively separate trend and seasonal components, and then incorporates phase information to improve the temporal alignment of periodic patterns.
DrafTS, a model-agnostic framework that aims to reduce noise while preserving evolving dynamics through time-aware Decomposition with ResiduAl correction For Time Series, is proposed.
A forecasting framework that explicitly models the coupling between trend and seasonality, and a heteroscedastic Laplace loss function that combines uncertainty weighting with heteroscedastic modeling, reducing the impact of error accumulation over long prediction horizons and enhancing robustness to outliers and heavy...
Long‐term time series forecasting (LTSF) plays a crucial role in practical applications such as power dispatching, financial market analysis, and climate prediction. To tackle core challenges in LTSF, including the difficulty of capturing nonlinear patterns and insufficient multi‐scale feature extraction, we propose a...
Accurate forecasting of multivariate time series (MTS) is foundational across critical domains including high-frequency clinical monitoring, power grid operations, financial modeling, and distributed industrial telemetry. However, real-world MTS signals exhibit severe distribution shifts, complex cross-channel dependen...
Frequency domain modeling methods have emerged as a promising research direction for long-term series forecasting, owing to their natural advantages in capturing global dependencies and reducing computational complexity. However, existing methods overlook the heterogeneity and dynamic characteristics of frequency prope...
Long-term time series forecasting is important in energy, transportation, and meteorology, yet existing methods struggle to model multi-scale features and preserve local sequence structure. We propose PatchRNN, a decomposition- and structure-aware framework for multivariate forecasting. Its parallel trend branch uses m...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
