Multiple time series

Multiple time series, also known as multivariate time series, is a collection of several variables observed over the same period. Originating from econometrics, its core concept is to model the dynamic interdependencies and lead-lag relationships between different variables. In risk management, it serves as a key quantitative tool, aligning with the ISO 31000:2018 requirement for systematic risk analysis. For instance, instead of only analyzing login failures (univariate), a multiple time series approach models how system vulnerabilities, abnormal login attempts, and security alerts jointly predict a data breach, providing a more holistic and realistic risk insight.

In enterprise risk management, multiple time series is applied to build predictive monitoring models. The implementation involves three key steps: 1. **Metric Identification & Data Collection:** Based on the NIST Cybersecurity Framework (CSF) 'Identify' function, define leading indicators for key risks (e.g., data breach), such as abnormal network traffic and phishing reports. Automate data collection to ensure quality. 2. **Model Building & Validation:** Use models like Vector Autoregression (VAR) to analyze interdependencies from historical data and validate the model's predictive accuracy. 3. **Prediction & Integration:** Deploy the model to generate future risk trend forecasts from real-time data. Integrate these quantitative insights into a risk dashboard to support decision-making, as guided by ISO/IEC 27005. A global financial firm improved its breach prediction accuracy by 35% using this method.

Taiwan enterprises face three main challenges when implementing multiple time series for risk prediction: 1. **Challenge: Data Silos and Poor Quality:** Data is often fragmented across different systems with inconsistent formats, making integration difficult. **Solution:** Establish a data governance framework and start with a small-scale proof-of-concept (PoC) data lake, focusing on 2-3 critical data sources. 2. **Challenge: Lack of Hybrid Talent:** The technique requires a blend of data science, statistics, and domain expertise, which is scarce. **Solution:** Adopt a hybrid approach by upskilling internal IT staff while partnering with external consultants like Winners Consulting to accelerate implementation. 3. **Challenge: Poor Model Interpretability:** Complex models can be 'black boxes,' making it hard to explain predictions to non-technical management, which hinders buy-in. **Solution:** Utilize Explainable AI (XAI) techniques and visualization dashboards to translate model outputs into intuitive risk scores and actionable insights.

Winners Consulting specializes in Multiple time series for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact