Scope of Submission
We welcome submissions from a broad range of disciplines, including but not limited to:
- 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
All papers must be original and not previously published or submitted elsewhere.
