Dynamic Neural Network Modeling and Contextual Enrichment of High Dimensional Multivariate Data: A Framework for Multimodal Augmentation and Analysis

Abstract

This dissertation addresses the complex challenge of analyzing and predicting High Dimensional Multivariate Data (HDMvD) across diverse domains, focusing on the integration of textual, visual, and structured time series data. The multifaceted nature of these data types, characterized by high dimensionality, non-stationarity, and intricate dependencies, poses significant challenges for traditional analytical methods. While recent advancements in deep learning have made strides, they often struggle to balance short-term fluctuations with long-term patterns and lack the flexibility to adapt to varying data types and temporal scales. To bridge these gaps, we propose a novel trilogy of approaches: the On-Demand Image for Textual Question Answering (OD-TQA) method, the BiDepth Multimodal Neural Network (BDMNN), and the Adaptive Depth Deep Neural Network (AdDepth), each building upon the insights of its predecessor to create a comprehensive framework for both unstructured and structured data analysis. Our methodology uniquely combines dynamic contextual information retrieval, innovative neural network architectures for capturing multi-scale temporal dependencies, and adaptive model structures that automatically adjust to the most relevant components of the data. Highlights of our approaches include the ability to use external knowledge to improve performance, seamless model adaptivity based on any given data, the introduction of a novel attention mechanism for time series data, and the use of reinforcement learning to optimize model depth dynamically. Empirical evaluations across multiple diverse datasets demonstrate statistically significant improvements in predictive accuracy and model efficiency, showcasing the potential of our approach for data analysis across a wide range of applications.