Publication

Non-linear prediction: a parsimonious family of predictors

1995
Conference Papers

Abstract

The analysis of an observed univariate time series is often undertaken in order to get a prediction of a future event. With this purpose one can fix a class of predictors from which the optimal one will be identified and estimated. The more simple and common choice is the linear family, that is linear combinations of the lags of the series. However, it is well known that considering non-linearities in the lags may improve the prediction. We introduce in this paper a class of non-linear predictors based on polynomials and neural network methodology. These predictors have both the advantages of being relatively simple to identify and of introducing non-linearity without increasing the number of estimated parameters by much compared to linear predictors

Official source

https://infoscience.epfl.ch/entities/publication/2d3659a4-875f-49fa-b792-da5517a3d1db

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

1995
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/2d3659a4-875f-49fa-b792-da5517a3d1db

About this result

Related concepts (32)

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Generalized linear model

In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value. Generalized linear models were formulated by John Nelder and Robert Wedderburn as a way of unifying various other statistical models, including linear regression, logistic regression and Poisson regression.

Linear discriminant analysis

Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.

Related publications (21)

Non-linear prediction: a parsimonious family of predictors

Chat with Graph Search

Linear analysis of the effect of shaping on trapped electron mode instability: from a reduced model to gyro-kinetics

Randomized Householder QR

Designing Sampling Strategies for the Efficient Estimation of Parameterized Spatial Covariance Models