Lightmorphic Signatures Analysis Toolkit
This is an incremental tool for researchers or practitioners working with lightmorphic data analysis, providing a modular and customizable software package.
The paper introduces LSAT, an open-source toolkit that tackles the tedious and error-prone task of converting lightmorphic data into spectrograms, using a self-supervised neural network and augmented machine learning algorithms to improve this process.
In this paper we discuss the theory used in the design of an open source lightmorphic signatures analysis toolkit (LSAT). In addition to providing a core functionality, the software package enables specific optimizations with its modular and customizable design. To promote its usage and inspire future contributions, LSAT is publicly available. By using a self-supervised neural network and augmented machine learning algorithms, LSAT provides an easy-to-use interface with ample documentation. The experiments demonstrate that LSAT improves the otherwise tedious and error-prone tasks of translating lightmorphic associated data into usable spectrograms, enhanced with parameter tuning and performance analysis. With the provided mathematical functions, LSAT validates the nonlinearity encountered in the data conversion process while ensuring suitability of the forecasting algorithms.