Call For Papers
The ICTSAPF 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 Probability Theory,Statistics, 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 modeling techniques
- Probabilistic forecasting methods
- Statistical analysis of time series
- Seasonal decomposition in forecasting
- Machine learning for time series
- Bayesian approaches to forecasting
- Temporal data mining techniques
- Longitudinal data analysis methods
- Autoregressive integrated moving average
- Forecasting with neural networks
- Causal inference in time series
- Real-time forecasting applications
- Time series anomaly detection
- Multivariate time series analysis
- Time series in economics
- Dynamic systems and forecasting
- Forecasting in climate science
- Time series and big data
- Statistical software for time series
- Emerging trends 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.