Papers Multivariate Time Series Forecasting
“Multivariate Time Series Forecasting” 태그가 달린 논문 327편 · 필터 해제
Fuzzy-MoE: Interpretable Regime-Conditioned Expert Routing for Non-Stationary Multivariate Time Series Forecasting
In non-stationary multivariate time series, different variables and samples often exhibit heterogeneous latent dynamic states, while existing deep forecasting models usually compress them into a unified end-to-end mappin…
Multivariate Time Series ForecastingCLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
Probabilistic forecasting models are widely used for time series forecasting in domains such as energy systems, finance, medicine, and transportation. In recent years, deep generative models have shown strong results on …
Probabilistic Time Series ForecastingMultivariate Time Series ForecastingRethinking Patch Based Multivariate Time Series Forecasting with Semantic Structured Partitioning
Multivariate time series forecasting (MTSF) is a fundamental task in many real world applications. Existing patch based forecasting methods generally fall into three categories: fixed partitioning, multi-scale partitioni…
Multivariate Time Series ForecastingAsyTO: Asymmetric Temporal Operator for Parameter-Efficient Multivariate Time Series Forecasting
Multivariate time-series forecasting faces a structural dilemma: sharing one temporal predictor across variables is parameter-efficient but forces heterogeneous variables through an identical history-to-future map, where…
Multivariate Time Series ForecastingMultivariate Time Series Forecasting needs Cross Variable Loss
Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics. While existing studies mainly focus on cross-variable dependencies in historical obse…
Multivariate Time Series ForecastingMulti-Scale Convolution with Optimal Transport Attention Effect on Multivariate Time Series
The analysis of Multivariate Time Series (MTS) plays an important role in a lot of real-world practical applications, but it still remains some challenging problem about capturing multi-granularity structural patterns an…
Multivariate Time Series ForecastingStateFlow: Dual-State Recurrent Modeling for Long-Horizon Time Series Forecasting
Long-horizon multivariate time series forecasting (LTSF) remains challenging due to non-stationarity, regime shifts, and error accumulation. The Variability-Aware Recursive Neural Network (VARNN) is designed to track suc…
Multivariate Time Series ForecastingGlobal Explanations for Multivariate Time Series Forecasting Models via $K$-Order Markov Approximations
While many explainable AI (XAI) methods have been proposed, most are not designed for time-series forecasting models and often rely on the implicit assumption that timestamp features are independent. This assumption igno…
Multivariate Time Series ForecastingSpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting
Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to conventional neural networks, demonstrating strong performance in computer vision and robotics. More recently, SNNs have been applied to t…
Multivariate Time Series ForecastingSPDM: Geometry-Modulated State Space Modeling with Manifold Constraints for Time Series Forecasting
Multivariate time series forecasting requires capturing the continuously evolving correlation structure among interacting variables. Existing state-space models process time series by scanning tokenized temporal or spati…
Multivariate Time Series ForecastingComputational EfficiencyREGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting
Training robust multivariate time series forecasting models requires large, diverse corpora, yet many real-world domains provide only a handful of observed sequences. Existing generators fail to resolve this mismatch: pr…
Multivariate Time Series ForecastingTiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching
Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitoring. Due to the diversity of data sources, time series exhibit divers…
Multivariate Time Series ForecastingVLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecasting
Out of distribution (OOD) events in multivariate time series forecasting are rare but often dominate real world risk, making average case forecasting insufficient for reliable deployment. Under standard average risk trai…
Multivariate Time Series ForecastingFAiT: Frequency-Aware Inverted Transformer for Multivariate Time Series Forecasting
While Transformer-based architectures have established themselves as a dominant paradigm in Multivariate Time Series Forecasting (MTSF), their core self-attention mechanism inherently functions as a low-pass filter, syst…
Multivariate Time Series ForecastingComputational EfficiencyOnline Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration
Irregular multivariate time series forecasting is critical in many real-world applications, where time series are irregularly sampled and exhibit dynamically evolving missingness patterns. Although existing methods perfo…
Multivariate Time Series ForecastingBeyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting
While previous research in multivariate time series forecasting has focused on developing complex holistic models, this work advocates for a shift toward a granular, component-level understanding of their impacts. We pro…
Multivariate Time Series ForecastingProbabilistic Multivariate Time Series Forecasting with Diffusion Copulas
Accurately assessing financial risk requires capturing both individual asset volatility and the complex, asymmetric dependence structures that emerge during extreme market events. While modern diffusion-based models have…
Multivariate Time Series ForecastingFRWKV+: Periodic-Aware Adaptive Gating for Frequency-Space Linear Time Series Forecasting
Accurate and efficient long-term multivariate time series forecasting requires capturing recurring temporal structure while keeping inference cheap across many variables and horizons. Frequency-space models represent lon…
Multivariate Time Series ForecastingSeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies
Instance normalization (IN) is widely used in non-stationary multivariate time series forecasting to reduce distribution shifts and highlight common patterns across samples. However, IN can over-smooth instance-specific …
Multivariate Time Series ForecastingWhat If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
Multivariate time series forecasting is critical in many real-world systems, and thus modeling cross-channel dependencies is essential. Although existing methods improve overall accuracy by enhancing representations and …
Multivariate Time Series Forecasting