Time Array-Based Modeling for the Integrated Interpretation of Multi-Source Data.

Industrial data are commonly interpreted as static point values such as sales volume, inventory level, exchange rate, or production output. However, point-value representations fail to capture the dynamic behavior of real systems, including trends, cyclic patterns, turning points, and volatility. This paper proposes a universal Time Array (TA) framework that reconstructs industrial data as ordered temporal structures rather than isolated numerical values. By organizing recent observations into structural arrays, the current state can be interpreted in terms of pattern, directionality, and instability. In addition, a similarity-based mechanism compares the current Time Array with historically analogous patterns, and utilizes their subsequent transitions to estimate future multi-step trajectories. Beyond conventional forecasting, this study introduces \textit{fmap}, a structural signal that represents the accumulated directional pressure of the current state relative to historical patterns. Experimental results show that the magnitude of \textit{fmap}, rather than its sign, is strongly associated with realized risk. High-magnitude \textit{fmap} indicates excessive structural pressure and increased likelihood of reversal or correction, while near-zero values correspond to instability and volatility expansion. This suggests that \textit{fmap} functions as a \textit{state stress signal} rather than a simple trend continuation indicator. Furthermore, user behavior analysis reveals that decision-making relies more on similarity to past structural patterns than on abstract numerical predictions. As a result, the proposed framework provides not only predictive trajectories but also actionable insights for operational decision-making. The proposed approach is validated across heterogeneous industrial domains, including retail, logistics, manufacturing, finance, and healthcare. The results demonstrate that transforming industrial data from static values into temporal structures, combined with risk-oriented interpretation through \textit{fmap}, provides a new foundation for forecasting, operational control, and real-time decision support.