Semiparametric averaging of nonlinear marginal logistic regressions and forecasting for time series classification
Semiparametric averaging of nonlinear marginal logistic regressions and forecasting for time series classification
Binary classification is an important issue in many applications but mostly studied for independent data in the literature. A binary time series classification is investigated by proposing a semiparametric procedure named “Model Averaging nonlinear MArginal LOgistic Regressions” (MAMaLoR) for binary time series data based on the time series information of predictor variables. The procedure involves approximating the logistic multivariate conditional regression function by combining low-dimensional non-parametric nonlinear marginal logistic regressions, in the sense of Kullback-Leibler distance. A time series conditional likelihood method is suggested for estimating the optimal averaging weights together with local maximum likelihood estimations of the nonparametric marginal time series logistic (auto)regressions. The asymptotic properties of the procedure are established under mild conditions on the time series observations that are of β-mixing property. The procedure is less computationally demanding and can avoid the “curse of dimensionality” for, and be easily applied to, high dimensional lagged information based nonlinear time series classification forecasting. The performances of the procedure are further confirmed both by Monte-Carlo simulation and an empirical study for market moving direction forecasting of the financial FTSE 100 index data.
Binary time series classification, Forecasting, Logistic marginal regression, MAMaLoR, Model average, Semi-parametric likelihood estimation
Peng, Rong
a82d230a-2ab9-4b41-993a-cd5eb21b41a7
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Peng, Rong
a82d230a-2ab9-4b41-993a-cd5eb21b41a7
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Peng, Rong and Lu, Zudi
(2021)
Semiparametric averaging of nonlinear marginal logistic regressions and forecasting for time series classification.
Econometrics and Statistics.
(doi:10.1016/j.ecosta.2021.11.001).
Abstract
Binary classification is an important issue in many applications but mostly studied for independent data in the literature. A binary time series classification is investigated by proposing a semiparametric procedure named “Model Averaging nonlinear MArginal LOgistic Regressions” (MAMaLoR) for binary time series data based on the time series information of predictor variables. The procedure involves approximating the logistic multivariate conditional regression function by combining low-dimensional non-parametric nonlinear marginal logistic regressions, in the sense of Kullback-Leibler distance. A time series conditional likelihood method is suggested for estimating the optimal averaging weights together with local maximum likelihood estimations of the nonparametric marginal time series logistic (auto)regressions. The asymptotic properties of the procedure are established under mild conditions on the time series observations that are of β-mixing property. The procedure is less computationally demanding and can avoid the “curse of dimensionality” for, and be easily applied to, high dimensional lagged information based nonlinear time series classification forecasting. The performances of the procedure are further confirmed both by Monte-Carlo simulation and an empirical study for market moving direction forecasting of the financial FTSE 100 index data.
Text
Semiparametric Averaging of Nonlinear Marginal Logistic Regressions and Forecasting for Time Series Classification
- Accepted Manuscript
More information
Accepted/In Press date: 13 November 2021
e-pub ahead of print date: 23 November 2021
Additional Information:
Funding Information:
The authors are grateful to the Editor-in-Chief Professor Erricos John Kontoghiorghes, the Associate Editor and two referees for their valuable and constructive comments and suggestion, which have greatly helped to improve the presentation of this paper.
Publisher Copyright:
© 2021 EcoSta Econometrics and Statistics
Keywords:
Binary time series classification, Forecasting, Logistic marginal regression, MAMaLoR, Model average, Semi-parametric likelihood estimation
Identifiers
Local EPrints ID: 452260
URI: http://eprints.soton.ac.uk/id/eprint/452260
ISSN: 2452-3062
PURE UUID: 3e8343ae-f4e1-473a-af0c-5242bb720546
Catalogue record
Date deposited: 02 Dec 2021 17:32
Last modified: 17 Mar 2024 06:57
Export record
Altmetrics
Contributors
Author:
Rong Peng
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
