A one-page flyer is available at:
https://www.mdpi.com/journal/mathematics/special_issue_flyer_pdf/81C08HUU6H/web
Dear Colleagues,
This Special Issue aims to address the challenges and
opportunities encountered while applying statistical and
data science approaches across diverse domains. Topics
including but not limited to:
1. Predictive modelling: employing statistical and data
science techniques to develop accurate predictive models
for applications such as financial forecasting, disease
prediction, customer behaviour analysis, and demand
forecasting.
2. Machine learning: Exploring the integration of statistical
principles and machine learning algorithms, including
classification, regression, clustering, and feature selection.
Topics also encompass model interpretability, fairness,
and robustness.
3. Big data analytics: Tackling challenges and leveraging
opportunities in analysing and extracting insights from
large-scale and high-dimensional datasets. Techniques of
interest include data pre-processing, dimensionality
reduction, distributed computing, and scalable algorithms.
4. Time series analysis: examining advanced statistical
techniques for modelling and forecasting time series data,
encompassing autoregressive models and state-space
models and handling seasonality and nonstationary