The growth of global networks and data-driven analyses have greatly fostered the evolution of business and economic services with e-mode operations in the past few decades. The recent advances in cloud environment, economic indicators, artificial intelligence and risk management have driven the development of the next generation of economic services featuring resilient, scalable, reliable, and intelligent services across diverse global institutions. The spurt in volume and size led to several challenges.
Firstly, the broad adoption of many business and economic platforms leads to massive and heterogeneous economic data, which brings enormous value and technical challenges. Secondly, decentralising economic services creates challenges in reliable and secure services. Specifically, existing information granaries across different financial services institutions also deteriorate this situation. Thirdly, it is a big task to implement resilient, scalable, and automatic financial services. Moreover, the recent advent of big financial data processing also brings the potential to develop traceable and reliable financial services. Thus, fusing the above technologies to accelerate data- based intelligent financial services becomes a trend.
The global economy has recently witnessed significant structural changes that have profoundly impacted how prices are determined on the market. Events like market liberalization, adoption of cost-efficiency regulation, increased production from large businesses and economic data have made the demand and supply less predictable and the prices more volatile. Accurately modelling and forecasting financial demand and prices have become paramount to financial management groups and financial analysts focusing on the economic sector. The statistical features of financial data, which follow periodic patterns and exhibit spikes, non-constant means and non-constant variances, render forecasting and modelling economic data somewhat an uphill task Researchers autonomously conduct financial and economic analyses. Researchers can evaluate which economic analysis model is more appropriate to the task and the robustness of the results obtained.
At this conference, the themes focus on society’s unique challenges and will present novel solutions for optimizing collaborative and forecasting models. More specifically, we aim to welcome papers in the following areas:
Economic data analysis
Time Series Models
Volatility Models
Financial Demand Models
Time Series forecasting
Outlook Indicators
Forecast Indicators
Data-driven analysis
Business Cycle Analysis
National Economic Forecasts
Fiscal and monetary policies
Economic Transition
Social Issues
Supply chain studies
Economic Forecasting in different sectors
Regulatory planning and scenario analysis
Risk identification and prioritization
All the accepted papers will be published in the proceedings with ISBN and DOI. Selected papers will be published after modification in the journals below.
Future Internet
https://www.mdpi.com/journal/futureinternet
Data
https://www.mdpi.com/journal/data
(For Data and Future Internet, the discounted APC is applicable, which is 50%)
Journal of Digital Information Management
Important Dates
| Submission of papers | August 31, 2024 |
| Notification | September 20, 2024 |
| Camera-ready | October 05, 2024 |
| Registration | October 05, 2024 |
| Conference Dates | October 17-19, 2024 |
Honorary Chair
Dương Văn Hiếu, Tien Giang University, Vietnam
General Chair
Ezendu Ariwa, University of Wales Trinity, UK
Program Chairs
K.Shivachithappa, University of Mysore. India
Rita Li, Hong Kong Shue Yan University. Hong Kong
Program Co-Chair
Pit Pichappan, Digital Information Research Labs, India
P Paramasiviah, Tumkur University, India
Contact– stm@socio.org.uk