TL;DR
EF-Transformer is proposed, an early forecasting framework designed to predict future values using the minimal possible lookback segment from the early input window, and achieved superior performance across a larger number of test cases compared to several recent State-of-the-Art methods.
Forecasting time series over long horizons is essential for proactive decision-making in many systems. Recent research has focused on transformer-based architectures, which capture long-range dependencies in sequential data. However, several studies show that simpler linear models can outperform transformers by avoidin...
SETTer is introduced, a transformer-based model that addresses challenges of long-term multivariate time series forecasting by incorporating novel techniques for decoupled self-attention and hybrid masking and outperforms state-of-the-art models in 88% of the scenarios.
This work proposes an inference-only streaming autoregressive framework that replaces repeated full-context recomputation with a one-time context warmup and incremental decoding, enabling efficient long-history forecasting without retraining.
SCALER is a coarse-to-fine forecasting framework that first employs a lightweight Transformer tailored to long-term shape modeling to predict a coarse representation of future dynamics, and reduces reliance on long description prompts, and its fixed-step refinement avoids costly reward-model-based selection, further lo...
This paper compares the three modeling paradigms for predictive process monitoring both conceptually and empirically through a controlled benchmark across multiple datasets and prediction tasks, showing that sequence models consistently perform best for next activity prediction, whereas tabular foundation models are co...
The Decomposed Recurrent Neural Network (DeRNN) is proposed, which decouples global trend modeling from local fluctuation extraction via an asymmetric dual-track architecture and exhibits superior robustness against noise and distribution shifts.
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