arXiv AI
Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Oct 7
Deep Time-Series Forecasting in 10 Years: A Survey
arXiv:2603.19899v2 Announce Type: replace-cross Abstract: Autocorrelation is a common property of time-series, where each observation is dependent on its predecessors. In deep time-series forecasting...
arXiv AI
Oct 7
FreDF: Learning to Forecast in the Frequency Domain
arXiv:2402.02399v3 Announce Type: replace-cross Abstract: Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences. While current research p...
arXiv AI
Oct 9
AliO: Output Alignment Matters in Long-Term Time Series Forecasing
AliO: Output Alignment Matters in Long-Term Time Series Forecasting proposes a new method, AliO, to improve output alignment in LTSF models by reducing discrepancies in prediction outputs for the same timestamps across both time and frequency domains. The authors introduce a Time Alignment Metric (TAM) to quantify alignment, contrasting with traditional MSE metrics that only measure distance to ground truth. Experiments demonstrate that AliO can increase TAM by up to 58.2% while maintaining or improving forecasting performance by up to 27.5%.
arXiv AI
Oct 7
DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
arXiv:2510.24574v3 Announce Type: replace-cross Abstract: Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence. Th...
arXiv Machine Learning
Aug 4
AROpt: An Optimization Method for Autoregressive Time Series Forecasting
arXiv:2602. 02288v3 Announce Type: replace Abstract: Current time-series forecasting models are primarily based on transformer-style neural networks.
arXiv AI
Aug 18
Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Representation Alignment
arXiv:2508. 07195v2 Announce Type: replace-cross Abstract: Recent advances have demonstrated that Large Language Models (LLMs) can be effectively adapted for time series forecasting, revealing strong potential beyond natural language tasks.