Applications and Analysis of Statistics and Data Science

Dear Colleagues,

Statistics and data science have revolutionised decision-making processes across various domains, enabling insight extraction and accurate predictions. From finance to healthcare and social sciences to technology, statistical methods and data science techniques have driven significant advancements. This Special Issue aims to delve into the wide-ranging applications and analysis of statistics and data science, focusing on advancements, challenges, and opportunities in this dynamic and ever-evolving field.

This Special Issue provides an inclusive platform for researchers to showcase their work and exchange knowledge on the applications and analysis of statistics and data science. The goal is to highlight innovative methodologies, novel techniques, and emerging trends that drive advancements in this field. Additionally, this Special Issue aims to address the challenges and opportunities encountered while applying statistical and data science approaches across diverse domains.

Topics of interest (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.

Dr. Jinran Wu
Prof. Dr. Fang Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• statistical learning
• machine learning
• deep learning
• artificial intelligence-based methods
• big data
• causality inference
• data fusion
• forecasting
• optimisation
• anomaly detection