TITLE:
A Study on Diversity Prediction with Machine Learning and Small Data
AUTHORS:
Rezza Moieni, Peter Mousaferiadis, Leyla Roohi
KEYWORDS:
Diversity, Data Forecasting, Small Data, Mutuality, Diversity Atlas
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.11 No.2,
February
9,
2023
ABSTRACT: There are discussions about the importance of
diversity in literature and in the media and minimizing gaps between minorities
and majorities. In order to see if a community is making progress in minimizing
these gaps and to measure success, there is an interest in being able to
predict the diversity of communities given currently prevailing. There are well-designed
data forecasting algorithms in data science using large data sets. However,
diversity data has only been collected over the last few decades. This paper
adopts algorithms formulated by Grey and ARIMA (Auto-Regressive Integrated
Moving Average), using small data to predict the likely diversity of a cohort
for a time in the near future. Our results
demonstrate there is more confident forecasting for “country of birth”, but in
terms of predicting linguistic and religious diversity, due to the changeable
nature of these factors throughout an individual’s life, we would require
further data to make any accurate prediction.