SMU Data Science Review SMU Data Science Review
This paper aims to develop a method to build an interpretable model for univariate and multivariate nonlinear time series data using wavelets and symbolic regression and relies on multilayer perceptron (MLP) neural networks as a form of dimensionality reduction and the PySR algorithm to determine the symbolic relationships.
Abstract
. Current nonlinear time series methods such as neural networks forecast well. However, they act as a black box and are difficult to interpret, leaving the researchers and the audience with little insight into why the forecasts are the way they are. There is a need for a method that forecasts accurately while also being easy to interpret. This paper aims to develop a method to build an interpretable model for univariate and multivariate nonlinear time series data using wavelets and symbolic regression. The final method relies on multilayer perceptron (MLP) neural networks as a form of dimensionality reduction and the PySR algorithm to determine the symbolic relationships. It also explores use cases for using the discrete wavelet transformation to extract information from the dataset.