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Design a robust traffic forecasting pipeline

AmazonData Scientist · Technical Screen
HardData ScientistTechnical ScreenMachine Learning

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This question tests the ability to design an end-to-end, scalable time-series forecasting pipeline, focusing on anomaly handling, structural decomposition, uncertainty quantification, and model validation strategies.

Problem: Designing a Traffic Forecasting Pipeline

You are tasked with building a production-ready forecasting system to predict hourly traffic volume for thousands of highway segments. The system must account for daily, weekly, and seasonal patterns, as well as irregular anomalies like accidents or road closures.

Requirements:

  1. Data Preprocessing & Anomaly Handling: How would you identify and treat outliers (e.g., sensor failure vs. actual traffic spikes) before feeding data into the model?
  2. Modeling Strategy: Propose an architecture using Unobserved Components Models (UCM). How do you represent trends, seasonality, and exogenous regressors within this framework?
  3. Uncertainty Quantification: The business requires probabilistic forecasts (quantiles) rather than just point estimates. How will your model generate these intervals, and how do you handle non-Gaussian noise?
  4. Validation: Describe a robust backtesting strategy suitable for time-series data. How do you evaluate the model if your primary goal is to minimize the Pinball Loss across multiple quantiles?
  5. Scalability: Given thousands of segments, how do you handle cross-series dependencies without retraining models from scratch for every sensor?

Evaluation Criteria:

  • Ability to articulate the trade-offs between additive and multiplicative components in UCM.
  • Understanding of rolling-origin cross-validation versus standard k-fold validation.
  • Depth in production-level considerations like model drift monitoring and retraining frequency.

Hints

Hint 1

Consider using state-space representation to allow the model to adapt to changing trends dynamically over time.

Hint 2

For anomaly detection, evaluate methods that distinguish between structural changes (level shifts) and temporary observation noise.

Hint 3

When evaluating probabilistic forecasts, focus on proper scoring rules like the Pinball Loss or Continuous Ranked Probability Score (CRPS) rather than RMSE.

Follow-up questions

  • How would your pipeline change if the data is highly intermittent (i.e., many zeros for rural segments)?
  • What strategies would you implement to detect if the model's performance has degraded in production due to changing traffic patterns?
  • How would you incorporate exogenous features like weather forecasts or holiday calendars into the UCM state-space framework?