Aggregation of predictors for nonstationary sub-linear processes and online adaptive forecasting of time varying autoregressive processes

DOI10.1214/15-AOS1345zbMath1327.62478arXiv1404.6769OpenAlexW2103872473MaRDI QIDQ892242

Christophe Giraud, Andres Sanchez-Perez, François Roueff

Publication date: 18 November 2015

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1404.6769

zbMATH Keywords

online learningnonstationary time seriesadaptive predictionexponential weighted aggregationtime varying autoregressive processes

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric inference (62G99) Online algorithms; streaming algorithms (68W27)

Related Items (9)

Prediction of weakly locally stationary processes by auto-regression ⋮ Simpler PAC-Bayesian bounds for hostile data ⋮ Aggregation of predictors for nonstationary sub-linear processes and online adaptive forecasting of time varying autoregressive processes ⋮ High-dimensional VAR with low-rank transition ⋮ ADAPTATION FOR NONPARAMETRIC ESTIMATORS OF LOCALLY STATIONARY PROCESSES ⋮ Functional Estimation and Change Detection for Nonstationary Time Series ⋮ Stochastic online convex optimization. Application to probabilistic time series forecasting ⋮ Time-frequency analysis of locally stationary Hawkes processes ⋮ Cross validation for locally stationary processes

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