A Genetic Algorithm to Optimize Lazy Learning Parameters for the Prediction of Customer Demands

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Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “Lazy learning-based approach has widely been used in several practical contexts, such as data streams [87], air quality planning [88], predictive toxicology based on a chemical ontology [89], prediction of customer demands [90], hydrologic forecasting [91], air quality prediction [92], magne-”
Meta-learning with neural networks and landmarking for forecasting model selection an empirical evaluation of different feature sets applied to industry data (2016, background): “The dynamical properties of the whole dynamical system can be reconstructed if two parameters are chosen appropriately, namely the embedding dimension m and the delay time τ [33], [34].”