AI Forecasting Analyst Interview Questions

Mention the key temporal dependency and the need for ordered data points.

Define it as regular, predictable patterns that repeat over a fixed period (e.g., daily, weekly, yearly).

To prevent look-ahead bias and simulate a realistic forecasting scenario where you predict the future from the past.

Mean Absolute Percentage Error; it is undefined when the actual value is zero and can be skewed by small actual values.

Statistical: ARIMA/SARIMA, Exponential Smoothing. ML: XGBoost, Random Forest.

Outline: data collection, EDA for trends/seasonality/events, choosing a baseline model (e.g., Prophet), feature engineering (holidays, promotions), model training with backtesting, evaluation, and deployment.

Discuss methods like forward-fill, backward-fill, interpolation, or using models that can handle missing data, cautioning against simplistic imputation that ignores temporal patterns.

A prediction interval gives a range within which the future value is expected to fall with a certain probability, quantifying uncertainty. It's more useful for risk-aware planning (e.g., inventory safety stock).

A stationary time series has constant mean and variance over time. ARIMA requires stationarity for reliable parameter estimation and forecasting.

Discuss using models like ARIMAX, Prophet with regressors, or feature-engineered ML models, emphasizing the need for forecasts of the regressors themselves.

Model-based uses domain knowledge to specify structure (e.g., economic models). Data-driven learns patterns from data. Choose model-based when the system is well-understood and data is limited; data-driven for complex systems with abundant data.

Forecasting at multiple levels (e.g., product, store, region) where forecasts must sum up coherently. Reconciliation methods ensure consistency.

Mention methods like transfer learning from similar products, using analogous time series, Bayesian methods with informative priors, or focusing on leading indicators.

Discuss monitoring performance metrics over time, using statistical tests for drift, and implementing automated retraining triggers based on performance decay.

Outline components: data ingestion/feature pipeline, experiment tracking, model training, containerized deployment (e.g., Docker on Kubernetes), real-time monitoring, and feedback loops for retraining.

Check for data pipeline issues (e.g., missing recent data), look for new external factors not in the model (e.g., new competitor), validate model assumptions, and communicate findings and potential model adjustments.

Discuss global models (e.g., DeepAR) that learn across all series, local models with feature grouping, or hierarchical approaches, weighing trade-offs.

Focus on models that handle volatility (e.g., GARCH for volatility modeling), incorporate event flags, use robust evaluation metrics, and emphasize the importance of probabilistic forecasts and scenario analysis.

Use a simple analogy (e.g., weather forecast), visualize the fan chart, and connect it directly to business risk (e.g., 'There's a 10% chance we sell less than X, so we should plan for that possibility').

Mention tools like MLflow or Weights & Biases to log parameters, metrics, and artifacts, and Git/DVC for versioning data and code.

For building a natural language interface to query forecasts or model results (e.g., 'Why was last month's forecast high?') by connecting an LLM to the forecasting system's logs and metadata.

Discuss the steps: preparing data in the required format (CSV with item_id, timestamp, target), uploading to S3, creating a dataset group, training a predictor, generating forecasts, and evaluating results via the console or SDK.

Consider window length (context), horizon (forecast length), feature scaling/normalization, and handling of variable-length sequences.

Look for reflection on root cause (e.g., missing data, structural change), accountability, and process improvements (e.g., better monitoring, more robust backtesting).

Emphasize use of clear visualizations, analogies, focusing on business implications rather than technical details, and checking for understanding.

Mention criteria like business impact (revenue, cost), decision criticality, data availability, and alignment with strategic goals.

Discuss defining data quality requirements, optimizing a feature pipeline, or designing a model serving API.

Mention sources like arXiv, relevant conferences (NeurIPS, ICML), industry blogs (Uber Engineering, Amazon Science), Kaggle competitions, and continuous learning courses.