Backward-in-Time Selection of the Order of Dynamic Regression Prediction Model

TL;DR

This paper introduces Backward-in-Time Selection (BTS), a new method for structuring dynamic regression models in multivariate time series prediction, which outperforms existing techniques in various scenarios.

Contribution

The paper proposes BTS, a novel lag selection scheme that effectively handles feedback and multi-collinearity, improving prediction accuracy over traditional methods.

Findings

BTS shows consistently superior prediction performance in simulations.

BTS outperforms other methods in EEG seizure prediction.

BTS achieves the best results in financial market index prediction.

Abstract

We investigate the optimal structure of dynamic regression models used in multivariate time series prediction and propose a scheme to form the lagged variable structure called Backward-in-Time Selection (BTS) that takes into account feedback and multi-collinearity, often present in multivariate time series. We compare BTS to other known methods, also in conjunction with regularization techniques used for the estimation of model parameters, namely principal components, partial least squares and ridge regression estimation. The predictive efficiency of the different models is assessed by means of Monte Carlo simulations for different settings of feedback and multi-collinearity. The results show that BTS has consistently good prediction performance while other popular methods have varying and often inferior performance. The prediction performance of BTS was also found the best when tested on human electroencephalograms of an epileptic seizure, and to the prediction of returns of indices of world financial markets.