# Modeling of time series using random forests: theoretical developments

@article{Davis2020ModelingOT, title={Modeling of time series using random forests: theoretical developments}, author={Richard A. Davis and Mikkel Slot Nielsen}, journal={ArXiv}, year={2020}, volume={abs/2008.02479} }

In this paper we study asymptotic properties of random forests within the framework of nonlinear time series modeling. While random forests have been successfully applied in various fields, the theoretical justification has not been considered for their use in a time series setting. Under mild conditions, we prove a uniform concentration inequality for regression trees built on nonlinear autoregressive processes and, subsequently, we use this result to prove consistency for a large class of…

## 7 Citations

Machine Learning Advances for Time Series Forecasting

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The most recent advances in supervised machine learning and highdimensional models for time series forecasting are surveyed and ensemble and hybrid models by combining ingredients from different alternatives are considered.

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This work recast the synthetic controls for evaluating policies as a counterfactual prediction problem and replaces its linear regression with a nonparametric model inspired by machine learning, and applies this method to a highly debated policy: the relocation of the US embassy to Jerusalem.

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An attempt has been made to explore efficient ML algorithms e.g. Generalized Neural Network (GRNN), Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machine (GBM) for forecasting wholesale price of Brinjal in seventeen major markets of Odisha, India and it is observed that GRNN performs better in most of the cases.

An approach to building a financial model for the purposes of planning resource products of the corporate segment in commercial banks

- Business, EconomicsFinance and Credit
- 2022

Subject. The paper considers planning of resource products in the corporate business segment of a commercial bank.
Objectives. The aim is to develop a model for planning financial results generated…

Targeting predictors in random forest regression

- EconomicsInternational Journal of Forecasting
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Random forest regression (RF) is an extremely popular tool for the analysis of high-dimensional data. Nonetheless, its benefits may be lessened in sparse settings, due to weak predictors, and a…

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