State of the Field
Recent advancements in time series forecasting are increasingly focused on enhancing model efficiency and accuracy while addressing the complexities of real-world data. New frameworks are emerging that prioritize long-term forecasting stability, such as those that utilize look-ahead augmentation and self-corrective refinement to improve predictive consistency. Additionally, linear models are gaining traction for their ability to reduce computational complexity, enabling faster inference without sacrificing performance. The introduction of global temporal retrieval mechanisms allows models to capture long-range dependencies more effectively, while dual-path architectures are being explored to separate autoregressive dynamics from cross-dimensional interactions, enhancing overall accuracy. Furthermore, the integration of mixture-of-experts approaches is proving effective in managing diverse temporal patterns, improving robustness against noise, and facilitating uncertainty quantification. These developments suggest a shift towards more interpretable and efficient forecasting solutions that can better serve industries reliant on accurate predictions, such as finance, healthcare, and education.
Papers
1–10 of 15Back to the Future: Look-ahead Augmentation and Parallel Self-Refinement for Time Series Forecasting
Long-term time series forecasting (LTSF) remains challenging due to the trade-off between parallel efficiency and sequential modeling of temporal coherence. Direct multi-step forecasting (DMS) methods...
Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval
Multivariate time series forecasting (MTSF) plays a vital role in numerous real-world applications, yet existing models remain constrained by their reliance on a limited historical context. This limit...
AltTS: A Dual-Path Framework with Alternating Optimization for Multivariate Time Series Forecasting
Multivariate time series forecasting involves two qualitatively distinct factors: (i) stable within-series autoregressive (AR) dynamics, and (ii) intermittent cross-dimension interactions that can bec...
Seg-MoE: Multi-Resolution Segment-wise Mixture-of-Experts for Time Series Forecasting Transformers
Transformer-based models have recently made significant advances in accurate time-series forecasting, but even these architectures struggle to scale efficiently while capturing long-term temporal dyna...
vLinear: A Powerful Linear Model for Multivariate Time Series Forecasting
In this paper, we present \textbf{vLinear}, an effective yet efficient \textbf{linear}-based multivariate time series forecaster featuring two components: the \textbf{v}ecTrans module and the WFMLoss ...
DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models
Time-series foundation models (TSFMs) have achieved strong univariate forecasting through large-scale pre-training, yet effectively extending this success to multivariate forecasting remains challengi...
SDMixer: Sparse Dual-Mixer for Time Series Forecasting
Multivariate time series forecasting is widely applied in fields such as transportation, energy, and finance. However, the data commonly suffers from issues of multi-scale characteristics, weak correl...
PatchDecomp: Interpretable Patch-Based Time Series Forecasting
Time series forecasting, which predicts future values from past observations, plays a central role in many domains and has driven the development of highly accurate neural network models. However, the...
StretchTime: Adaptive Time Series Forecasting via Symplectic Attention
Transformer architectures have established strong baselines in time series forecasting, yet they typically rely on positional encodings that assume uniform, index-based temporal progression. However, ...
DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting
Deep time series models are vulnerable to noisy data ubiquitous in real-world applications. Existing robustness strategies either prune data or rely on costly prior quantification, failing to balance ...