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Brownlee, J. (2018). Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery.

has been cited by the following article:

• TITLE: Small Scale Predictive Analysis of Gender Balance in Australia Using Grey Models: Integrating Labour Force and Migration Data

  AUTHORS: Eloisa Rios, Shurong Hou, Nicole Lee, Rezza Moieni

  KEYWORDS: Gender Balance, Overseas Migration, Australia Labour Market, Diversity in Workplace, Becker’s Discrimination Coefficient (D), ARIMA Model, Grey Model, MAPE

  JOURNAL NAME: Open Journal of Social Sciences, Vol.12 No.7, July 29, 2024

  ABSTRACT: Gender balance is a key part of the Australian identity, for creating diverse workplaces and fostering social cohesion throughout Australia. This study aims to provide a comprehensive understanding of gender balance in Australia by exploring the labour force and overseas migrations for equality, diversity, and inclusion place. The research proposed a Gender Balance (GB) index metric based on Becker’s coefficient considering labour force and migration data to measure GB index. With small dataset comprising a total of 16 data points for each Australian state, covering from 2004 to 2022 were used to forecast GB index for the next five years. Arima, Grey Model GM (1, 1) and GM (2, 1) were used as forecasting models. The research revealed GM (1, 1) to be the optimal model to forecast gender balance index trends. The findings can inform policy decisions and interventions to promote greater gender equality and equity nationwide.