Autoregressive Integrated Moving Average

Autoregressive Integrated Moving Average (ARIMA) is a statistical model combining autoregressive (AR), integrated (I), and moving average (MA) components to analyze and forecast time series data. Originally developed by Box and Jenkins, it remains a cornerstone of predictive analytics. In the context of ISO 22301 Business Continuity Management (BCM), ARIMA serves as a quantitative tool for demand forecasting and trend-adjusted risk assessment. Unlike black-box AI models, ARIMA provides transparent statistical assumptions, making it suitable for regulated industries where model interpretability is required for compliance. It differs from deep learning models by requiring fewer data points but demands rigorous stationarity checks, which is a critical step in any robust risk-adjusted forecasting framework.

ARIMA application in ERM typically follows a three-stage process: Data Preparation, Model Identification, and Risk-Adjusted Forecasting. In the preparation stage, historical data is transformed into a stationary series through differencing. In the identification stage, parameters p, d, and q are optimized using criteria like AIC or BIC. Finally, the model generates forecasts with confidence intervals, which are mapped against the company's Risk Tolerance levels. For instance, a Taiwan-based electronics manufacturer can use ARIMA to forecast semiconductor lead times, enabling them to trigger a BCP-mandated supplier diversification strategy before a predicted shortage occurs. Companies implementing this approach have reported a 20-30% improvement in forecast accuracy and a significant reduction in stock-out risks during peak demand periods.

Taiwan enterprises face three primary challenges: Data Quality, Technical Expertise, and Extreme Event Resilience. Many SMEs lack the historical data-gathering infrastructure necessary for accurate ARIMA modeling. The solution is to invest in digital transformation and data-centric governance. Secondly, the shortage of data-literate risk managers can be addressed through professional training or partnerships with firms like Winners Consulting Services Co., Ltd. Lastly, ARIMA's inability to account for structural breaks (e.g., sudden regulatory changes or pandemics) requires a hybrid approach—combining ARIMA with NIST-aligned scenario planning. The recommended priority is to first audit existing data-gathering capabilities, then pilot the model on a single critical business function, and finally scale it across the enterprise within 12 months.

Winners Consulting Services Co., Ltd. specializes in Autoregressive Integrated Moving Average for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact