Adaptive Multi-Scale Hypergraph Transformer (Ada-MSHyper) for Enhanced Time Series Forecasting by Modeling Group-wise Temporal Pattern Interactions
Ada-MSHyper, a novel deep learning model, leverages adaptive multi-scale hypergraphs and transformers to improve time series forecasting accuracy by effectively capturing complex group-wise interactions within and across different temporal scales.
Sequential Order-Robust Mamba: An Efficient Time Series Forecasting Method for Handling Channel Dependencies
SOR-Mamba is a novel time series forecasting method that leverages a regularized, unidirectional Mamba architecture and a channel correlation modeling pretraining task to effectively and efficiently capture channel dependencies in time series data, outperforming existing state-of-the-art methods.
DisenTS: A Framework for Improving Multivariate Time Series Forecasting by Modeling Disentangled Channel Evolving Patterns
DisenTS, a novel framework leveraging multiple distinct forecasting models, enhances multivariate time series forecasting by implicitly disentangling and modeling diverse channel evolving patterns.
WaveRoRA: A Novel Approach to Multivariate Time Series Forecasting Using Wavelet Transform and Rotary Route Attention
WaveRoRA is a novel model for multivariate time series forecasting that leverages wavelet transform to capture time-frequency characteristics and a novel Rotary Route Attention (RoRA) mechanism to efficiently model inter-series dependencies, achieving state-of-the-art performance with lower computational costs.
Spectral Attention for Improved Long-Range Dependency Modeling in Time Series Forecasting
This paper introduces Spectral Attention, a novel mechanism that enhances time series forecasting models by enabling them to effectively capture and utilize long-range dependencies in sequential data, leading to improved prediction accuracy.
TripCast: A Novel 2D Transformer Model for Forecasting Tourism Time Series Data using Pre-training
TripCast, a novel pre-trained 2D transformer model, effectively addresses the unique challenges of forecasting tourism time series data by considering both event time and leading time dependencies, outperforming existing methods in both in-domain and out-domain scenarios.
Frequency Interpolation for Time Series Forecasting: A Comprehensive Review and Critique of FITS
FITS, a novel time series forecasting model using frequency domain interpolation, demonstrates competitive performance with significantly reduced parameters compared to state-of-the-art models, particularly excelling in capturing periodic and seasonal patterns, but exhibiting limitations in handling trending or non-periodic behaviors.
LiNo: A Novel Time Series Forecasting Framework Using Recursive Residual Decomposition of Linear and Nonlinear Patterns
LiNo, a novel time series forecasting framework, leverages recursive residual decomposition to effectively separate and model linear and nonlinear patterns in time series data, leading to more accurate and robust predictions.
xLSTM-Mixer: An Effective Recurrent Model for Long-Term Multivariate Time Series Forecasting
xLSTM-Mixer, a novel recurrent neural network architecture, achieves state-of-the-art performance in long-term multivariate time series forecasting by effectively integrating time, variate, and multi-view mixing within an xLSTM framework.
HiPPO-KAN: A Parameter-Efficient Model for Time Series Analysis Using High-order Polynomial Projection and Kolmogorov-Arnold Networks
HiPPO-KAN, a novel neural network architecture combining High-order Polynomial Projection (HiPPO) and Kolmogorov-Arnold Networks (KAN), offers a parameter-efficient and scalable solution for time series analysis, outperforming traditional methods in accuracy and efficiency, especially for long-range forecasting.
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