Fractal interpolation in the context of prediction accuracy optimization

arXiv:2403.00403v1 Announce Type: new
Abstract: This paper focuses on the hypothesis of optimizing time series predictions using fractal interpolation techniques. In general, the accuracy of machine learning model predictions is closely related to the quality and quantitative aspects of the data used, following the principle of \textit{garbage-in, garbage-out}. In order to quantitatively and qualitatively augment datasets, one of the most prevalent concerns of data scientists is to generate synthetic data, which should follow as closely as possible the actual pattern …

abstract accuracy arxiv context cs.lg data fractal general hypothesis machine machine learning machine learning model optimization paper prediction predictions quality quantitative series time series type