Author(s)

Rezza Moieni, Peter Mousaferiadis, Leyla Roohi

Cultural Infusion Pty, Ltd., Melbourne, Australia.

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.

Diversity, Data Forecasting, Small Data, Mutuality, Diversity Atlas

Moieni, R. , Mousaferiadis, P. and Roohi, L. (2023) A Study on Diversity Prediction with Machine Learning and Small Data. Open Journal of Social Sciences, 11, 18-31. doi: 10.4236/jss.2023.112002.