Time-series data featurization

Abstract

The present disclosure relates to methods, systems, and apparatuses for featurizing time-series data to enhance machine learning model training. Time-series data, such as transaction records, is preprocessed to identify fields including descriptive and categorical information. Categories are assigned using a first machine learning model, and tags are applied based on domain-specific patterns or large language models. The processed data is organized into a star schema data structure comprising a fact structure and associated dimension structures. Features are generated from the data structure based on time windows, incorporating statistical metrics and identified patterns. These features are provided to a machine learning module to train a second machine learning model, improving accuracy and adaptability for applications such as customer behavior prediction and financial analysis. The disclosed approach addresses challenges of high dimensionality, noise, and temporal dependencies in time-series data, enabling robust and contextually relevant feature generation.