Time Series Analysis and Forecasting

Neueste Paper

• Wavelet Flow Matching for Time Series

  Lucas Poinsignon, Jorge da Silva Gon\c{c}alves, Samuel Ruip\'erez-Campillo, Julia E. Vogt · 1. Oktober 2026

  Synthetic time series are increasingly used for data augmentation, privacy-preserving data sharing, and downstream model development, yet faithfully reproducing both multi-scale temporal structure and cross-channel dependencies remains challenging. We study multivariate time-series generation throug…

• WinoTS: Wavelet-based Self-Distillation for Time Series Models

  Noam Major, Kathy Razmadze, Yoli Shavit · 1. Oktober 2026

  Self-supervised pre-training of time series models is currently dominated by next-token prediction and reconstruction objectives. In continuous-valued domains, these paradigms often waste model capacity on high-frequency, point-wise noise at the expense of learning invariant structure. While invaria…

• A Time-Aware Bag-of-Receptive-Fields for Interpretable Irregular Time Series Classification

  Francesco Spinnato · 1. Oktober 2026

  Irregular time series, characterized by non-uniform sampling intervals, missing observations, and variable lengths, are ubiquitous in healthcare, mobility, and environmental monitoring, yet effective and interpretable classifiers for this setting are limited. Existing approaches often rely on imputa…

• Cyclostationary Phase Conditioning for Medical Time Series Diffusion

  Samuel Ruiperez-Campillo, Michele Copetti, Jorge da Silva Goncalves, Sonia Laguna, Thomas Hofmann, Julia E. Vogt · 30. September 2026

  Many physiological time series, such as cardiac and brain recordings, exhibit cyclostationarity: their statistics vary periodically with an underlying cycle phase. Corruption from motion, poor contact, and physiological interference obscures morphology needed for diagnosis, making signal restoration…

• Rethinking Patch Based Multivariate Time Series Forecasting with Semantic Structured Partitioning

  Zhiquan Huang, Jiazhe Wang, Linjing Xue, Ming Liu, Meiwen Li, Ruijuan Zheng · 30. September 2026

  Multivariate time series forecasting (MTSF) is a fundamental task in many real world applications. Existing patch based forecasting methods generally fall into three categories: fixed partitioning, multi-scale partitioning, and extendable partitioning. Fixed partitioning often breaks meaningful temp…

• ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation

  Fariza Rashid, Duc Van Le, Rahat Masood, Gustavo Batista, Aruna Seneviratne, Suranga Seneviratne · 30. September 2026

  Time series imputation has progressed from statistical and deep learning approaches to diffusion-based models, which have shown strong recent performance. Existing diffusion-based methods typically condition the reverse process using local contextual information from the current or neighbouring wind…

• Channel-Dependent State Space Model for Multivariate Time Series Forecasting

  Yu-Cheng Wu, Fan-Keng Sun, Li-Chun Lu, Duane S. Boning · 30. September 2026

  Multivariate time series forecasting (MTSF) is critical across many real-world domains. Existing deep learning approaches fall into two paradigms with distinct limitations: channel-independent (CI) methods unconditionally ignore cross-variable dependencies and model only temporal dynamics, while cha…

• Domain Generalization under Sampling Pattern Shifts in Irregular Time Series

  Changhun Kim, Joohyung Lee, Kwanhyung Lee, Donghwee Yoon, Grigorios Chrysos, Eunho Yang · 29. September 2026

  Irregularly sampled multivariate time series (ISMTS) are prevalent in real-world applications, where both observation times and available measurements can vary substantially across domains. While recent models increasingly exploit such sampling information for prediction, its robustness under sampli…

• Rondo: Unsupervised Discovery of Recurring Temporal Structure

  Yingtian Shi, Ankith Chandra, Thomas Pl\"otz · 29. September 2026

  Many real-world time series data exhibit structural properties at multiple scales, from short, recurring units to complex sequences composed of these units. Unsupervised discovery of both these components and structure enables the design of intelligent systems that help interpret temporal data, ther…

• TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra

  Weiwei Ye, Renhe Jiang, Hangchen Liu, Dongyuan Li, Yoshihide Sekimoto · 29. September 2026

  Real-world time series are inherently non-stationary, with trends, periodic patterns, and uncertainty evolving over time. While the Fourier domain offers a natural lens to model time series, current deep learning approaches do not explicitly model evolution and randomness in the Fourier spectra, whi…

• Does Joint-Embedding Predictive Architecture Pretraining Help Time Series Forecasting?

  Yutong Feng, Bowen Liao, See Kiong Ng, Yuxuan Liang · 29. September 2026

  Joint-embedding predictive architectures (JEPA) have emerged as a promising self-supervised pretraining paradigm for time series, learning representations by predicting target embeddings in latent space rather than reconstructing raw signals. Yet evidence on their benefits remains mixed, and most st…

• DrafTS: Time-Aware Decomposition with Residual Correction for Time Series Modeling

  Yiqiu Liu, Siru Zhong, Zhiguang Wang, Qingsong Wen, Yuxuan Liang · 29. September 2026

  Real-world time series contain evolving underlying dynamics with irregular variations that lack stable temporal patterns and are often referred to as noise. Existing methods address this mixture by filtering frequencies or suppressing noisy observations. They either miss temporal evolution or risk s…

• ChronoFlow: Hierarchical Flow Matching for Irregular Time Series Generation

  Changhun Kim, Sunguk Jang, Jeongjun Lee, Juhwan Choi, Sangchul Hahn, Grigorios Chrysos, Eunho Yang, Juho Lee · 29. September 2026

  Recent advances in generative modeling have substantially improved time series generation, yet most existing methods either assume a regular temporal grid or focus on feature dynamics under a given sampling structure. This makes them illsuited for generating irregular time series in their native for…

• MixBench-TS: A Multivariate Time Series Forecasting Benchmark Where Channel Mixing Pays Off

  Ibram Abdelmalak, Mischa Putzke, Jungmin Choi, Tom Hanika, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme · 29. September 2026

  Multivariate Time Series Forecasting (MTSF) models that mix information across channels assume that the past of one channel carries information about the future of another. Yet they are evaluated on a small fixed set of standard datasets whose cross-channel structure is rarely examined. We ask two q…

• Progressive Memory Transformer: Memory-Aware Attention for Time-Series

  Tord Sture Stangeland, Andreas K\"ohler, Steffen M{\ae}land, Ad\'in Ram\'ires Rivera · 28. September 2026

  Time-series carry structure simultaneously at multiple scales (fine-grained variation, mid-range motifs, and global properties) and downstream tasks operate at correspondingly different scales. Most existing self-supervised learning approaches supervise representations globally via instance-level co…

• Aurora-X: Built for Extreme Time Series Forecasting

  Xingjian Wu, Chenjuan Guo, Xiangfei Qiu, Zhigang Hu, Hanyin Cheng, Peng Chen, Yang Shu, Jilin Hu, Bin Yang · 28. September 2026

  Time series foundation models (TSFMs) enable cross-domain forecasting, but their development as general-purpose forecasters remains constrained by underexplored training potential and limited architectural versatility. To address these challenges, we introduce Aurora-X, a billion-scale TSFM with a p…

• SwitchPFN: Shared Switching Dynamics for Frozen In-Context Time Series Classification

  Zhenyi Zhu, Jacqueline Pang, Peilin Shen, Tianyi Song, Tingwei Zhang, Keyi Hu, Kangjun Yin, Shiwei Pu, Yingbo Zhou, Chen Shao · 25. September 2026

  Tabular foundation models (TFMs) provide a promising route to time-series classification, but their effectiveness depends on how sequential data are converted into tabular representations. Existing representations face two challenges: global aggregation can lose the order of temporal evolution, whil…

• TimeBraid: Unifying Time Series and Language for Understanding and Forecasting

  Xinyue Wang, Jiacheng Pang, Kun Zhou, Kexin Zhang, Defu Cao, Fan Feng, Faisal, Songyao Jin, Yan Liu, Biwei Huang · 25. September 2026

  We present TimeBraid, a series of unified time-series and language models that align pretrained language models and pretrained time-series foundation models through interleaved global residual attention layers. Each model inherits knowledge, instruction following, and reasoning from one side, contin…

• Neuralized Multi-Wavelet Decomposition for Time Series Classification and Forecasting

  Xiaohan Jiang, Jingyuan Wang, Jiahao Ji, Yongyao Wang, Chen Yang, Junjie Wu · 25. September 2026

  Time series analysis is fundamental in domains such as finance, healthcare, and meteorology. Real-world time series often exhibit multiscale characteristics shaped by diverse latent factors, resulting in intricate temporal patterns and rich frequency structures. However, existing approaches typicall…

• TimeEvo: Failure-Driven Self-Evolution of a Time Series Agent

  Jie Yang, Yan Zheng, Jiarui Sun, Xiran Fan, Junpeng Wang, Liang Wang, Zelin Xu, Qinghua Liu, Zhengyu Fang, Yiwei Cai, Philip S. Yu · 24. September 2026

  Time series agents answer analytical questions by calling external tools, and which tools they carry is decided by people before the agent runs. However, we identify two failures in this setup. Human-Agent Tool Misalignment: a library of 21 expert-curated tools helps on some tasks and hurts on other…

• Learning Where to Look: A Shared Relative-Alignment Module for Time-Series Forecasting and PPG-to-Vital-Sign Reconstruction

  Ragamayi Puli, Shunya Nagashima · 24. September 2026

  PPG-to-vital-sign reconstruction turns a wrist-worn photoplethysmogram into clinical waveforms such as the ECG. Long-horizon multivariate time-series forecasting underpins planning in energy, weather, and traffic. Both generate a target sequence from a condition sequence, and current models hard-cod…

• Spatiotemporal Kronecker Covariance Neural Networks

  Andrea Cavallo, Athanasios Georgoutsos, Elvin Isufi · 23. September 2026

  Multivariate time series contain complex patterns that span across both space and time. While covariance-based statistical tools like spatiotemporal Principal Component Analysis (ST-PCA) help identify these patterns, they are limited to linear operations and prone to estimation errors with limited d…

• TimeInteract: Towards Real-Time Interactive Intelligence for Streaming Time Series

  Sheng Pan, Yongli Gu, Yiqing Guo, Warren Jin, Bo Du, Shirui Pan, Ming Jin · 23. September 2026

  Real-world time series evolve continuously, with meaningful changes potentially emerging at any moment. However, existing time-series language models (TSLMs) remain inherently static. They either receive complete sequences for offline processing or alternate between streaming input and response gene…

• Overlay\_dx - Automating forecasting evaluation

  Long Ngo, Mohammed Amine Chamli, Jonathan Rivalan, Thomas Jaillon · 22. September 2026

  Traditional evaluation metrics provides numerical values but often lack comprehensibility, hindering effective differentiation of model performances. Our work addresses this challenge by introducing overlay\_dx, a novel evaluation metric measuring the performance of time series prediction models. Ov…

• MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecasting

  Xinying Cai, Junkai Lu, Yuhan Zhu, Xiaoyun Yu, Xiangfei Qiu, Jilin Hu · 22. September 2026

  Multivariate time-series forecasting is essential to many real-world applications. Recent large vision models (LVMs) offer a promising paradigm by transferring cross-domain visual priors to time-series forecasting. However, existing LVM-based methods face two key challenges: balancing independent vi…