AdaMamba introduces an adaptive frequency-gated Mamba architecture for long‑term time series forecasting. The model integrates an interaction learning module that captures cross‑variable dependencies and multi‑scale context, and an adaptive frequency‑gated state space module that generates context‑dependent frequency bases and unifies temporal forgetting into a single time‑frequency gate. Experiments on seven public benchmarks and four domain‑specific datasets show that AdaMamba consistently outperforms state‑of‑the‑art baselines while remaining computationally efficient.
By Xudong Jiang, Mingshan Loo, Wengen Li, Mingrui Zhang, Jihong Guan, Shuigeng Zhou
arXiv:2603. 15506v2 Announce Type: replace-cross Abstract: We argue that the current practice of evaluating AI/ML time-series forecasting models, predominantly on benchmarks characterized by strong, persistent periodicities and seasonalities, obscures real progress by overlooking the performance of efficient classical methods.
By Raeid Saqur, Christoph Bergmeir, Blanka Horvath, Daniel Schmidt, Frank Rudzicz, Terry Lyons
arXiv:2608. 14682v1 Announce Type: new Abstract: Real-world time series inherently intertwine global periodic structures with localized non-stationary variations.
By Gaofeng Lin, Lei Duan
FLAME is a lightweight Time Series Foundation Model that uses Legendre Memory variants (LegT and LegS) in its encoding and decoding stages to capture inductive biases and perform efficient long‑range forecasting. It incorporates a normalizing‑flow forecasting head to generate complex probabilistic distributions over future horizons. Experiments on TSFM‑Bench, ProbTS, and TFB show FLAME performs strongly as an out‑of‑the‑box tool for decision intelligence.
By Xingjian Wu, Zhengyu Li, Hanyin Cheng, Xiangfei Qiu, Jilin Hu, Chenjuan Guo, Bin Yang
arXiv:2606. 06010v1 Announce Type: new Abstract: Long-term time series forecasting benefits from inductive biases that expose recurring temporal structure.
By Zhangyao Song, Ziqiong Li, Xiangfei Qiu, Chao Zha, Yinfei Xu, Tao Guo
arXiv:2610.32189v1 Announce Type: new
Abstract: Unsupervised multivariate time series anomaly prediction (AP) aims to estimate future anomaly risks from current observations, providing early-warning...
By Jianxin Jin, Kai Zhao, Shiyan Hu, Yang Shu, Peng Chen, Meng Wang, Zhongwen Rao, Yijie Li, Jing Wang, Bin Yang, Chenjuan Guo
The paper introduces AdaSpecK, a spectral-Koopman framework designed for Temporal Domain Generalization (TDG). It combines spectral-regularized Koopman dynamics modeling to filter noise in latent space with a context-informed heterogeneous pattern extraction mechanism that uses target-conditioned attention and a learned router to summarize relevant past windows. Experiments on eight classification and regression benchmarks show that AdaSpecK achieves state‑of‑the‑art performance.
By Tengxue Zhang, Yu Ke, Yang Shu, Chenchen Sun, Yisheng An, Chenjuan Guo, Bin Yang
arXiv:2607. 00154v1 Announce Type: cross Abstract: Evolutionary neural architecture design for multivariate time-series forecasting remains underexplored, with most approaches relying on fixed Transformer architectures despite substantial variation across tasks and forecasting settings.
By AbdElRahman ElSaid, Damir Pulatov
arXiv:2607. 19382v1 Announce Type: cross Abstract: Deep learning models have shown strong potential for time series forecasting, yet their deployment in real-world environmental monitoring remains challenging due to non-stationary dynamics and limited explainability.
By Quentin Besnard (RFAI), Emmanuel Doumard (BDTLN), Nicolas Labroche (LIFAT, BDTLN), Nicolas Ragot (RFAI), Nicolas Ringuet (BDTLN)
arXiv:2607. 00470v1 Announce Type: cross Abstract: We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time.
By Agnieszka Kope\'c, Pawe{\l} Przyby{\l}owicz, Martyna Wi\k{a}cek
arXiv:2605. 11287v2 Announce Type: replace-cross Abstract: A persistent paradox in time-series forecasting is that structurally simple MLP and linear models often outperform high-capacity Transformers.
By Jevon Twitty, Vinh Pham, Nitiwith Rotchanarak, Viresh Pati, Yubin Kim, Shihao Yang, Jiecheng Lu
The paper introduces TimeFT, a new tokenizer for time series that operates in the frequency domain to avoid spectral distortions caused by traditional subsequence partitioning. It uses frequency-domain partitioning, frequency shifting, and Nyquist sampling, is parameter‑free, and adds negligible computational overhead. Experiments across forecasting, classification, and anomaly detection show consistent and significant performance gains over existing tokenizers.
By Zelong Tian, Ming Jin, Bo Du, Shirui Pan