Explain the importance of model selection criteria in time series analysis.
Instruction: Discuss various criteria for model selection in time series analysis and their importance in building accurate and reliable models.
Updated
Example Answer
Model selection should reflect the forecasting decision, not just historical fit. I would compare plausible candidates with a baseline at the required horizon, inspect residual problems, and include deployment cost and maintainability in the decision.
Information criteria can screen comparable fitted models, while rolling-origin evaluation tests their forecasts at several past dates. They answer related but different questions. For example, a slightly better information criterion may not justify a more expensive model if it adds no useful forecast improvement. I would choose the error measure based on the cost of mistakes and reserve a final test period after tuning. I would also report weak periods and structural changes, rather than claim one score guarantees future accuracy.
Make it your own
Name the planning decision, horizon, baseline, and acceptable complexity tradeoff.
Why this works
It gives an evidence-based selection process without invented business outcomes.
Follow-up to practice
What would you choose if information criteria and backtests disagree?