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Publication# Non-linear prediction: a parsimonious family of predictors

1995

Conference paper

Conference paper

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

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Related publications (1)

Related concepts (6)

Prediction

A prediction (Latin præ-, "before," and dicere, "to say"), or forecast, is a statement about a future event or data. They are often, but not always, based upon experience or knowledge. There is no universal agreement about the exact difference from "estimation"; different authors and disciplines ascribe different connotations. Future events are necessarily uncertain, so guaranteed accurate information about the future is impossible. Prediction can be useful to assist in making plans about possible developments.

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.

Time series

In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. A time series is very frequently plotted via a run chart (which is a temporal line chart).

This paper describes inferences based on linear predictors for stationary time series. These methods are flexible, since relatively few assumptions are needed to fit a linear predictor. A confidence i

1993