Time Series Analysis - Frontiers in Research and Practice offers a concise overview of cutting-edge methods for understanding, forecasting, and applying models to temporal data. The volume combines Markov Chain Monte Carlo, Bayesian ARIMA, and Posterior Predictive Distribution to enable uncertainty-aware time series forecasting. It reinterprets temporal data through co-evolving time series, causal dependencies, shift/visibility graph representations, graph representation learning, multiplex networks, and temporal metagraphs, tools suited for dependency regime forecasting and critical fields such as cybersecurity. Bridging theory and application, it advances data-driven prediction using Takens’ embedding theorem, dynamic models, neural network algorithms, and demonstrates deployment-ready, efficient pipelines via neuro-symbolic techniques for edge computing, integrating spectral signal processing, pattern recognition, signal symbolization, and convolutional neural networks. Additionally, it features compression-based prediction with universal coding and decision trees, providing transparent and robust baselines. The book’s strengths include a unified cross-disciplinary view, focus on interpretability and uncertainty, guidance for real-time, resource-limited environments, and practical examples (such as numerical and dislocation modeling in geoscience) that turn techniques into actionable insights. Geared toward researchers, practitioners, and postgraduate students, this edited volume provides a modern, interoperable toolkit linking rigorous modeling with real-world applications and decision-making.