arXiv AI
A Lightweight Multi-View Approach to Short-Term Load Forecasting
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arXiv Machine Learning
Oct 28
STeP-TS: Shared Temporal Filters with Pruning for Time Series
The paper introduces STeP-TS, a compact one‑layer convolutional model for multivariate time‑series forecasting that learns a shared bank of multi‑scale temporal filters for each variable. Lightweight variable‑specific scalar weights modulate the contribution of each filter, allowing the model to capture both shared temporal patterns and variable‑specific dynamics. The learned weights and filter‑output energy are used for structured pruning, reducing computational complexity with minimal loss in performance, and an amplitude‑adaptive normalization preserves variable‑dependent temporal amplitudes.
arXiv Machine Learning
Jun 11
CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting
arXiv:2511. 09789v2 Announce Type: replace Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predictions and interpretable insights into temporal dynamics.
arXiv Machine Learning
Sep 18
SETTer: Sparse-Encoder Transformer for Long-term Multivariate Time Series Forecasting
SETTer is a transformer-based model designed for long‑term multivariate time‑series forecasting. It introduces decoupled self‑attention and hybrid masking to better handle high dimensionality and complex relationships, while adding explainable structures to highlight discriminative patterns. Experiments on real‑world benchmarks show that SETTer outperforms state‑of‑the‑art models in 88% of scenarios.
arXiv Machine Learning
Jul 9
Rethinking Multimodal Time-Series Forecasting Evaluation
arXiv:2607. 06973v1 Announce Type: new Abstract: We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX.
arXiv AI
Aug 25
NeST: Neighborhood-aware semantic alignment and temporal modulation for LLM based time series forecasting
NeST is a framework that adapts large language models (LLMs) for continuous time‑series forecasting by creating neighborhood‑aware text prototypes and aligning them with temporal representations through a nearest‑neighbor contrastive objective. It retrieves the most relevant prototypes and uses them to conditionally modulate time‑series features, enabling more effective integration of textual and temporal information. Experiments show that NeST outperforms state‑of‑the‑art methods on eight benchmarks, reduces MSE by 1.2% for long‑term forecasting, improves zero‑shot forecasting by 4.9%, and boosts R² by 3.3% on a real‑world photovoltaic power forecasting task.
Hugging Face Trending Papers
Oct 19
CTLite: Lightweight Irregular Multivariate Time Series Forecasting with Reusable Continuous-Time Summaries
Irregular multivariate time series forecasting (IMTSF) requires capturing temporal dynamics and cross-variable dependencies from sparse, asynchronous observations. Existing methods often rely on learn...