ENBIS-24 Conference

Session

Machine learning for time series

16 Sept 2024, 14:35

Leuven, Belgium

Conveners

Machine learning for time series

• Biagio Palumbo (University of Naples Federico II)

71. Pattern matching for multivariate time series forecasting

Noé Lebreton (Université Lyon 2)

16/09/2024, 14:35

Predictive Analytics

The aim of pattern matching is to identify specific patterns in historical time series data to predict future values. Many pattern matching methods are non-parametric and based on finding nearest neighbors. This type of method is founded on the assumption that past patterns can be repeated and provide informations about future trends. Most of the methods proposed in the literature are...

33. Multivariate Singular Spectrum Analysis by Robust Diagonalwise Low-Rank Approximation

Dr Fabio Centofanti (University of Naples Federico II)

16/09/2024, 14:55

Machine Learning

Multivariate Singular Spectrum Analysis (MSSA) is a nonparametric tool for time series analysis widely used across finance, healthcare, ecology, and engineering. Traditional MSSA depends on singular value decomposition that is highly susceptible to outliers. We introduce a robust version of MSSA, named Robust Diagonalwise Estimation of SSA (RODESSA), that is able to resist both cellwise and...

92. Physical explanations of AI-time series forecasting using BAPC

ALFREDO LOPEZ (Software Competence Center Hagenberg SCCH)

16/09/2024, 15:15

Machine Learning

Our previous contribution to ENBIS included an introduction of BAPC (‘Before and After correction Parameter Comparison’), a framework for explainable AI time series forecasting, which has formerly been applied to logistic regression. An initially non-interpretable predictive model (such as neural network) to improve the forecast of a classical time series ’base model’ is used. Explainability...