Modeling of time series using random forests: theoretical developments

DOI10.1214/20-EJS1758zbMath1454.62256arXiv2008.02479OpenAlexW3092175230MaRDI QIDQ2209824

Yanyan Li

Publication date: 5 November 2020

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2008.02479

zbMATH Keywords

Markov processesnonparametric regressionrandom forestsnonlinear autoregressive models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric estimation (62G05) Stationary stochastic processes (60G10) Markov processes: estimation; hidden Markov models (62M05) Discrete-time Markov processes on general state spaces (60J05)

Uses Software

• GeneSrF

Cites Work

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• Bagging predictors
• On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification
• Uniform convergence of Vapnik-Chervonenkis classes under ergodic sampling
• Covariance inequalities for strongly mixing processes
• Consistency of random forests
• A random forest guided tour
• Comments on: ``A random forest guided tour
• Bernstein inequality and moderate deviations under strong mixing conditions
• Variance reduction in purely random forests
• A Review of Nonparametric Time Series Analysis
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Random Forests and Adaptive Nearest Neighbors
• Extremely randomized trees
• Random forests

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