Key papers

• A Multi-Task End-to-End Multivariate Long-Sequence Time Series Prediction Model for Load Forecasting (2026)
  Ziyuan Zhang et al.
  12.51
• A Model of the Fed's View on Inflation (2020)
  Thomas Hasenzagl et al.
  9.47
• Noise-Aware Shrinkage for Differentially Private Zeroth-Order Fine-Tuning of Large Language Models (2026)
  Lele Zheng et al.
  5.54
• ReasonCast: Towards Explainable Time Series Forecasting with Reasoning (2026)
  Seunghan Lee et al.
  5.51
• SPECTRA: Band-Routed Embedding and Stage-Wise LoRA for Cross-Sensor Fine-Tuning of Geospatial Foundation Models (2026)
  Xingyan Li et al.
  5.42
• A tensor network approach for chaotic time series prediction (2025)
  Rodrigo Mart\'inez-Pe\~na et al.
  5.40
• Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting (2026)
  Seunghan Lee et al.
  4.81
• How Much Does a Reasoning Summary Reveal? An Observability Ladder for Large Language Models (2026)
  Andres Algaba et al.
  4.81
• PAC Approximation and DIRECT Optimization for Parametric Markov Models (2026)
  Zhiming Chi et al.
  4.81
• POEM: Phase-Aware SO(2) Feature Rotation for Time Series Forecasting Under Periodicity Drift (2026)
  Jiawen Zhu et al.
  4.81
• Multiple cyclicity and Wavelet Decomposition with Channel Correlation for Long-term Time Series Forecasting (2026)
  Bin Wang et al.
  4.70
• Dynamic State Space Models With Temporal Adaptation for Time-Varying Sequence Modeling (2026)
  Tongyi Liang et al.
  4.55
• Modular Deep Learning for Multivariate Time-Series: Decoupling Imputation and Downstream Tasks (2024)
  Joseph Arul Raj et al.
  4.52
• T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation (2026)
  Dongik Park et al.
  4.43
• Linear Multi-Timescale Retention as a Memory-Efficient Vision-Language Bridge (2026)
  Ashfak Yeafi et al.
  3.77
• Scenario Analysis with Multivariate Bayesian Machine Learning Models (2025)
  Michael Pfarrhofer et al.
  2.88
• TriP-LLM: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection (2025)
  Yuan-Cheng Yu et al.
  2.84
• Revisiting Multivariate Time Series Forecasting with Missing Values (2025)
  Jie Yang et al.
  2.79
• Fourier Head: Helping Large Language Models Learn Complex Probability Distributions (2024)
  Nate Gillman et al.
  2.49
• Physics-Informed Recurrent Architecture with Embedded Thermodynamic Dynamics for Robust Sequence Modeling (2026)
  Zafer Yiğit et al.
  1.89
• Machine Learning Methods and Hybrid Models in Supply Chain Forecasting (2026)
  Marek Lewiński
  1.89
• Forecasting Source Stability in Scientific Experiments using Temporal Learning Models: A Case Study from Tritium Monitoring (2026)
  Nicholas Tan Jerome et al.
  1.83
• MambaSL: Exploring Single-Layer Mamba for Time Series Classification (2026)
  Yoo-Min Jung et al.
  1.78
• MRMS-Net and LMRMS-Net: Scalable Multi-Representation Multi-Scale Networks for Time Series Classification (2026)
  Celal Alag\"oz et al.
  1.72
• The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification (2026)
  Matthew Middlehurst et al.
  1.72
• Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM (2026)
  Hyunwoo Kim et al.
  1.72
• Hybrid Quantum Temporal Convolutional Networks (2026)
  Junghoon Justin Park et al.
  1.67
• TFEC: Multivariate Time-Series Clustering via Temporal-Frequency Enhanced Contrastive Learning (2026)
  Zexi Tan and Tao Xie and Haoyi Xiao and Baoyao Yang and Yuzhu Ji and An Zeng and Xiang Zhang and Yiqun Zhang
  1.61
• Efficient Time-Series Approximation with Linear Recurrent Neural Networks: Architecture Learning and Predictive Power (2018)
  Frieder Stolzenburg et al.
  1.60
• Deep Switching State Space Model (DS³M) for Nonlinear Time Series Forecasting with Regime Switching (2021)
  Xiuqin Xu et al.
  1.60
• The Meta-Learning Gap: Combining Hydra and Quant for Large-Scale Time Series Classification (2025)
  Urav Maniar
  1.56
• Forking-Sequences (2025)
  Willa Potosnak et al.
  1.44
• Long-Sequence Memory with Temporal Kernels and Dense Hopfield Functionals (2025)
  Ahmed Farooq
  1.28
• Mortality Forecasting under Climate Risk: A Stochastic Approach with Distributed Lag Non-Linear Models (2025)
  Jiacheng Min et al.
  1.22
• Less is More: Efficient Weight Farcasting with 1-Layer Neural Network (2025)
  Xiao Shou et al.
  1.17
• Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models (2025)
  Antoine Leclerc et al.
  1.11
• Oscillatory State-Space Models (2024)
  T. Konstantin Rusch et al.
  0.89
• SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation (2024)
  Hongfan Gao et al.
  0.89
• Autocorrelated errors in experimental data in the language sciences: Some solutions offered by Generalized Additive Mixed Models (2016)
  R. Harald Baayen and Jacolien van Rij and Cecile de Cat and Simon N. Wood
  —
• Identification of Risk Extreme Values in a Time Series and Analysis with an Autoregressive Method - Application for Climate Risk Events (2016)
  Gianluca Rosso
  —
• A regional compound Poisson process for hurricane and tropical storm damage (2016)
  Simon Mak et al.
  —
• Lasso Guarantees for Time Series Estimation Under Subgaussian Tails and β -Mixing (2016)
  Kam Chung Wong et al.
  —
• Lasso estimation for GEFCom2014 probabilistic electric load forecasting (2016)
  Florian Ziel and Bidong Liu
  —
• Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies (2016)
  Francois W. Belletti et al.
  —
• Watch-n-Patch: Unsupervised Learning of Actions and Relations (2016)
  Chenxia Wu et al.
  —
• Learning Network of Multivariate Hawkes Processes: A Time Series Approach (2016)
  Jalal Etesami et al.
  —
• Local Parametric Estimation in High Frequency Data (2016)
  Yoann Potiron et al.
  —
• Spatio-temporal Modelling of Temperature Fields in the Pacific Northwest (2016)
  Camila M. Casquilho-Resende et al.
  —
• SINOMA - A new approach for estimating linear relationships between noisy serial data streams (2016)
  Barnim Thees et al.
  —
• Sparse transition matrix estimation for high-dimensional and locally stationary vector autoregressive models (2016)
  Xin Ding et al.
  —
• Classification of Human Whole-Body Motion using Hidden Markov Models (2016)
  Matthias Plappert
  —
• Inference of High-dimensional Autoregressive Generalized Linear Models (2016)
  Eric C. Hall and Garvesh Raskutti and Rebecca Willett
  —
• Predicting Unemployment Claims Using Regional and Exogenous Signals: A Sparse Modeling Approach (2016)
  Avleen S. Bijral et al.
  —
• Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA (2016)
  Aapo Hyvarinen and Hiroshi Morioka
  —
• Forecasting wind power - Modeling periodic and non-linear effects under conditional heteroscedasticity (2016)
  Florian Ziel et al.
  —
• Towards stationary time-vertex signal processing (2016)
  Nathanael Perraudin and Andreas Loukas and Francesco Grassi and Pierre Vandergheynst
  —
• A Bayesian hierarchical model for monthly maxima of instantaneous flow (2016)
  Egil Ferkingstad et al.
  —
• California Reservoir Drought Sensitivity and Exhaustion Risk Using Statistical Graphical Models (2016)
  Armeen Taeb et al.
  —
• Bayesian forecasting and scalable multivariate volatility analysis using simultaneous graphical dynamic models (2016)
  Lutz F. Gruber and Mike West
  —
• Estimating trends in the global mean temperature record (2016)
  Andrew Poppick et al.
  —