A Symbolic Approach to Nonlinear Time Series Analysis

Authors

Ranjan Karki, Southern Methodist UniversityFollow
Nibhrat Lohia, Southern Methodist UniversityFollow
Michael B. Schulte, Southern Methodist UniversityFollow

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.

Recommended Citation

Karki, Ranjan; Lohia, Nibhrat; and Schulte, Michael B. (2024) "A Symbolic Approach to Nonlinear Time Series Analysis," SMU Data Science Review: Vol. 8: No. 1, Article 11.
Available at: https://scholar.smu.edu/datasciencereview/vol8/iss1/11

Creative Commons License

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