Key papers

• Bounds in Wasserstein Distance for Locally Stationary Functional Time Series (2025)
  Jan Nino G. Tinio et al.
  3.70
• Wasserstein multivariate auto-regressive models for modeling distributional time series (2022)
  Yiye Jiang et al.
  3.58
• Enhanced Random Subspace Local Projections for High-Dimensional Time Series Analysis (2026)
  Eman Khalid et al.
  1.78
• Multiple-group (Controlled) Interrupted Time Series Analysis with Higher-Order Autoregressive Errors: A Simulation Study Comparing Newey-West and Prais-Winsten Methods (2026)
  Ariel Linden
  1.78
• On Statistical Inference for High-Dimensional Binary Time Series (2025)
  Dehao Dai et al.
  1.61
• Learning Conditional Independence Differential Graphs From Time-Dependent Data (2025)
  Jitendra K Tugnait
  1.61
• Scalable Unsupervised Segmentation via Random Fourier Feature-based Gaussian Process (2025)
  Issei Saito et al.
  1.33
• Rethinking Nonstationarity in Time Series: A Deterministic Trend Perspective (2025)
  Zhandos Abdikhadir et al.
  1.28
• Interpretable Time Series Autoregression for Periodicity Quantification (2025)
  Xinyu Chen et al.
  1.28
• Topology-driven identification of repetitions in multi-variate time series (2025)
  Simon Schindler and Elias Steffen Reich and Saverio Messineo and Simon Hoher and Stefan Huber
  1.22
• Estimation methods of Matrix-valued AR model (2025)
  Kamil Ko{\l}odziejski (Institute of Mathematics et al.
  1.22
• Bayesian Shrinkage in High-Dimensional VAR Models: A Comparative Study (2025)
  Harrison Katz and Robert E. Weiss
  1.17
• Generalized network autoregressive modelling of longitudinal networks with application to presidential elections in the USA (2025)
  Guy Nason et al.
  1.11
• Parameter estimation for fractional autoregressive process with seasonal structure (2025)
  Chunhao Cai and Yiwu Shang
  1.11
• Recursive Gaussian Process State Space Model (2024)
  Tengjie Zheng et al.
  0.94
• Short-term time series prediction using Hilbert space embeddings of autoregressive processes (2016)
  Edgar A. Valencia and Mauricio A. \'Alvarez
  —
• Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula (2016)
  Donya Rahmani et al.
  —
• Analysis of nonstationary modulated time series with applications to oceanographic flow measurements (2016)
  Arthur P. Guillaumin and Adam M. Sykulski and Sofia C. Olhede and Jeffrey J. Early and Jonathan M. Lilly
  —
• PSF : Introduction to R Package for Pattern Sequence Based Forecasting Algorithm (2016)
  Neeraj Bokde et al.
  —
• Fractal approach towards power-law coherency to measure cross-correlations between time series (2016)
  Ladislav Kristoufek
  —
• Gaussian Process Kernels for Popular State-Space Time Series Models (2016)
  Alexander Grigorievskiy et al.
  —
• Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes (2016)
  Luca Ambrogioni and Eric Maris
  —
• Infinite Mixture Model of Markov Chains (2017)
  Jan Reubold et al.
  —
• Interpretable Vector AutoRegressions with Exogenous Time Series (2017)
  Ines Wilms et al.
  —
• Learning Causally-Generated Stationary Time Series (2018)
  Wessel Bruinsma and Richard E. Turner
  —
• Model Agnostic Time Series Analysis via Matrix Estimation (2018)
  Anish Agarwal et al.
  —
• Index Set Fourier Series Features for Approximating Multi-dimensional Periodic Kernels (2018)
  Anthony Tompkins and Fabio Ramos
  —
• Stochastic Gradient MCMC for State Space Models (2018)
  Christopher Aicher et al.
  —
• A new time-varying model for forecasting long-memory series (2018)
  Luisa Bisaglia and Matteo Grigoletto
  —
• GRATIS: GeneRAting TIme Series with diverse and controllable characteristics (2019)
  Yanfei Kang et al.
  —
• Why Are the ARIMA and SARIMA not Sufficient (2019)
  Shixiong Wang et al.
  —
• On Selecting Stable Predictors in Time Series Models (2019)
  Avleen S. Bijral
  —
• Time-warping invariants of multidimensional time series (2019)
  Joscha Diehl et al.
  —
• Improved GM(1,1) model based on Simpson formula and its applications (2019)
  Xin Ma et al.
  —
• Application of Levy Processes in Modelling (Geodetic) Time Series With Mixed Spectra (2019)
  J.P. Montillet et al.
  —
• Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality (2019)
  Saurabh Khanna and Vincent Y. F. Tan
  —
• Bayesian Median Autoregression for Robust Time Series Forecasting (2020)
  Zijian Zeng et al.
  —
• Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering (2020)
  Yohan Jung et al.
  —
• A comparison of Hurst exponent estimators in long-range dependent curve time series (2020)
  Han Lin Shang
  —
• Semiparametric time series models driven by latent factor (2020)
  Gisele O. Maia et al.
  —
• Statistical Estimation of High-Dimensional Vector Autoregressive Models (2020)
  Jonas Krampe and Efstathios Paparoditis
  —
• Interpretable Super-Resolution via a Learned Time-Series Representation (2020)
  Randall Balestriero et al.
  —
• On Multivariate Singular Spectrum Analysis and its Variants (2020)
  Anish Agarwal et al.
  —
• Forecasting with Multiple Seasonality (2020)
  Tianyang Xie et al.
  —
• An Improved Online Penalty Parameter Selection Procedure for $\ell_1$-Penalized Autoregressive with Exogenous Variables (2020)
  William B. Nicholson et al.
  —
• A Renormalization Group Approach to Connect Discrete- and Continuous-Time Descriptions of Gaussian Processes (2021)
  Federica Ferretti et al.
  —
• Traffic Flows Analysis in High-Speed Computer Networks Using Time Series (2021)
  G. Mill\'an
  —
• Statistical inference for ARTFIMA time series with stable innovations (2021)
  Jinu Kabala et al.
  —
• Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series (2021)
  Sylvain Calinon
  —
• Granger Causality: A Review and Recent Advances (2021)
  Ali Shojaie and Emily B. Fox
  —
• Time series models with infinite-order partial copula dependence (2021)
  Martin Bladt and Alexander J. McNeil
  —
• EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data (2021)
  Florian Haselbeck and Dominik G. Grimm
  —
• On Sparse High-Dimensional Graphical Model Learning For Dependent Time Series (2021)
  Jitendra K. Tugnait
  —
• Toeplitz Least Squares Problems, Fast Algorithms and Big Data (2021)
  Ali Eshragh et al.
  —
• STD: A Seasonal-Trend-Dispersion Decomposition of Time Series (2022)
  Grzegorz Dudek
  —
• Sparse-Group Log-Sum Penalized Graphical Model Learning For Time Series (2022)
  Jitendra K Tugnait
  —
• Network Vector Autoregressive Model for Dyadic Response Variables (2022)
  Jiajia Wang
  —
• Parametric quantile autoregressive moving average models with exogenous terms applied to Walmart sales data (2022)
  Alan Dasilva et al.
  —
• Modified Galton-Watson processes with immigration under an alternative offspring mechanism (2022)
  Wagner Barreto-Souza et al.
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• An Auto-Regressive Formulation for Smoothing and Moving Mean with Exponentially Tapered Windows (2022)
  Kaan Gokcesu et al.
  —