Backward-in-Time Selection of the Order of Dynamic Regression Prediction Model
This work addresses the challenge of improving prediction accuracy in multivariate time series for applications like medical monitoring and finance, representing an incremental advance in model selection techniques.
The paper tackles the problem of selecting optimal lag structures in dynamic regression models for multivariate time series prediction by proposing Backward-in-Time Selection (BTS), which accounts for feedback and multi-collinearity. Results from Monte Carlo simulations and real-world tests on epileptic EEG and financial market data show that BTS consistently outperforms other methods in prediction performance.
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.