arXiv AI
Naga: Vedic Encoding for Deep State Space Models
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arXiv AI
Sep 15
Parameter-Efficient Adaptation of Pretrained Language Models for Time-Series Forecasting
arXiv:2609.15344v1 Announce Type: new Abstract: We study the adaptation of pretrained language models to univariate time-series forecasting through a parameter-efficient transfer learning framework,...
arXiv AI
Oct 15
TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting
arXiv:2610.17864v1 Announce Type: cross Abstract: Long-term time series forecasting requires temporal dependency modeling that remains stable and generalizable under temporal distribution shifts. How...
arXiv AI
Sep 30
Channel-Dependent State Space Model for Multivariate Time Series Forecasting
The paper introduces Chameleon, a channel‑dependent state space model for multivariate time series forecasting that allows data‑dependent, fine‑grained interactions across variables while maintaining linear scaling with the number of variables. By integrating selective state space models with a Kalman filter and adapting GatedDeltaNet as the backbone, Chameleon improves generalization and achieves lower MSE and MAE on strongly dependent ODE and PEMS datasets compared to both channel‑independent and prior channel‑dependent methods. Across 28 benchmark settings, it outperforms baselines in the majority of cases and demonstrates competitive training‑time and memory efficiency on Traffic and ETT datasets.
arXiv Machine Learning
Aug 6
Echo Flow Networks
arXiv:2509. 24122v3 Announce Type: replace Abstract: At the heart of time-series forecasting (TSF) lies a fundamental challenge: how can models efficiently and effectively capture long-range temporal dependencies across ever-growing sequences?
arXiv AI
Jun 16
FlowState: Sampling-Rate-Equivariant Time-Series Forecasting
arXiv:2508. 05287v3 Announce Type: replace-cross Abstract: Existing time series foundation models (TSFMs), often based on transformer variants, lack adaptability to different sampling rates, struggle with generalization across varying context and target lengths, and are computationally inefficient.
arXiv AI
Aug 7
TS-RAG: Retrieval Augmented Generation for Time Series Forecasting
arXiv:2608. 06223v1 Announce Type: new Abstract: While deep learning models, particularly transformer-based architectures, have shown impressive performance in time series forecasting, the application of retrieval-augmented generation (RAG) in this domain remains limited.