Charting the Unseen: Transformative Approaches in Time Series Forecasting in Real World Domains

Abstract

This paper addresses the growing complexity of forecasting in an era where data volumes are expected to reach 180 zettabytes by 2025. It aims to bridge the gap between the theoretical aspects of time series analysis and their practical applications in fields like finance, healthcare, and environmental studies. The research covers foundational concepts such as Autocorrelation and White Noise and spans various methodologies from traditional models like ARIMA to advanced techniques involving machine learning. Special attention is given to the challenges of applying these theories to real-world, often irregular or incomplete data. The paper also explores the integration of technologies like AI in forecasting, emphasizing the need for robust and interpretable models. Concluding with a call for greater academia-industry collaboration, it suggests new research directions for innovative, practical forecasting solutions in a data-intensive world.