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International Conference on Applied Time Series and Forecasting Methods - (ICATSFM-27)

21st - 22nd June 2027 , Munich - Germany

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Call For Papers

The (ICATSFM-27) emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Statistics, Data Science are explored to promote cross-domain knowledge exchange and collaborative innovation.

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

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.

Registration

Registering for the conference provides access to keynote sessions, technical presentations, and networking opportunities with global experts.

Publication

Publishing through the conference enhances the visibility of your research and connects your work with a broader academic audience.

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