Call For Papers
The ICATSFM aims to explore emerging trends and future directions in research and innovation. It provides a collaborative platform for researchers and professionals to share ideas that shape the future of their respective domains.
The conference highlights advancements in Statistics,Data Science, encouraging innovative, solution-oriented research that addresses global challenges and technological evolution.
Authors are invited to submit papers addressing, but not limited to, the following areas:
- Time series forecasting methods and applications
- Statistical modeling of temporal data
- Seasonal decomposition in time series analysis
- ARIMA models for time series forecasting
- Statistical methods for financial time series
- Time series analysis in environmental studies
- Machine learning techniques for time series
- Statistical methods for anomaly detection in time series
- Longitudinal data analysis techniques
- Statistical software for time series analysis
- Causal inference in time series data
- Applications of time series in public health
- Statistical challenges in high-frequency data
- Time series regression modeling approaches
- Forecasting with multivariate time series
- Statistical methods for economic time series
- Time series analysis in social sciences
- Bayesian approaches to time series forecasting
- Statistical techniques for real-time forecasting
- Future directions in time series analysis
Assessment
Submissions will be assessed for originality, innovation, and relevance. Accepted papers will be presented at the conference and considered for publication opportunities in reputed academic platforms.
Registration
Participants are requested to complete the registration process following acceptance of their paper. Registration ensures inclusion in the conference schedule and official records.
Publication
All accepted manuscripts will be eligible for publication consideration in conference proceedings and associated academic journals.