Paper Info
Title
Multilinear Weighted Regression (MWE) with Neural Networks for trend prediction.
Abstract
The ability to define accurate linear models to find patterns or relationships between variables is one of the most challenging fields in Computer Science. In particular, extrapolative applications are widely used to predict values in Biological, Behavioral and Social Sciences. Analysts usually focus on reducing the approximation error in order to ensure fairly reliable predictions. Using pattern detection models such as Multilayer Artificial Neural Networks can offer good results in predicting possible outcomes, however they might not be the optimal fit for predicting evolutionary trends due to their inherent overfitting characteristics. Regression models, on the other hand are more often used to predict trending behaviors and therefore they can be more helpful when studying the evolution of a given instrument, symbol or series. However, a simple regression model can be very inaccurate with an unacceptable prediction error rate. This paper shows an automatic method to find trends in known instruments, by using a massive linear regression technique combined with a conventional machine learning proposal that works on optimizing the weights of the linear regressive structures. In this paper the authors show an application of the model in finance (stocks market instruments), however the proposal is designed to work for to any discipline that studies the trends of any evolutionary object.
Year
DOI
Venue
2019
10.1016/j.asoc.2019.105555
Applied Soft Computing
Keywords
Field
DocType
62M45,74PXX,68TXX
Regression analysis,Linear model,Unit-weighted regression,Artificial intelligence,Simple linear regression,Overfitting,Artificial neural network,Multilinear map,Mathematics,Machine learning,Linear regression
Journal
Volume
ISSN
Citations 
82
1568-4946
1
PageRank 
References 
Authors
0.35
0
3
Name
Order
Citations
PageRank
Alberto Arteta1143.41
Luis Fernando de Mingo López210.35
Nuria Gómez Blas310.35