Time Series Forecasting for Capacity Management

From reactive infrastructure to predictive operations. How Machine Learning is transforming capacity planning in telecom

Network traffic is growing, patterns are becoming more complex, and the cost of getting capacity wrong, in either direction, has never been higher. This webinar cuts through the noise to show how modern time series forecasting methods are already being used in production telecom environments to predict demand, prevent degradation, and reduce infrastructure waste. Whether you're just exploring Machine Learning driven forecasting or looking to mature your existing approach, you'll leave with practical insights you can apply directly.

What You'll Learn:

• Why Classical Forecasting Methods Are Hitting Their Limits

• A Practical Guide to Modern Forecasting Architectures

• Making Forecasts That Operations Teams Can Trust

• Solving Real Production Challenges: Drift, Retraining and AIOps Integration

• The Rise of Time Series Foundation Models
  

Real-World Use Cases:

• ISP and mobile network traffic forecasting for proactive capacity scaling

• Automated model retraining triggered by concept drift in live systems

• Cloud resource utilization forecasting to optimize infrastructure costs in dynamic environments