What factors are contributing to the sudden increase in error rates for DataRobot's time series forecasting models this month?

Introduction

The sudden increase in error rates for DataRobot's time series forecasting models this month presents a critical challenge that requires immediate attention and a systematic approach to resolution. As we delve into this issue, we'll employ a structured framework to identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications.

Our analysis will focus on understanding the factors contributing to this performance degradation, examining both internal and external variables that could be impacting our time series forecasting models. We'll begin by clarifying the context, then systematically explore potential causes, formulate hypotheses, and develop a comprehensive plan for resolution.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timing, I'm thinking there might be a recent change in our data pipeline. Have there been any updates to our data ingestion or preprocessing systems in the past month?

Why it matters: Changes in data handling could directly impact model performance. Expected answer: Yes, there was a recent update to our data preprocessing pipeline. Impact on approach: If confirmed, we'd focus on validating the data quality post-update.

• Considering the scope, I'm curious about the extent of the issue. Is this increase in error rates consistent across all time series models or specific to certain types or industries?

Why it matters: Helps determine if it's a systemic issue or limited to specific use cases. Expected answer: The issue is more pronounced in certain industries or model types. Impact on approach: We'd prioritize investigating those specific areas first.

• Given the nature of time series forecasting, I'm wondering about any significant external events. Have there been any major market disruptions or unusual events in the past month that could affect our forecasts?

Why it matters: External factors can significantly impact time series predictions. Expected answer: There have been some unexpected market fluctuations in certain sectors. Impact on approach: We'd need to assess our models' ability to adapt to these changes.

• Thinking about our user base, has there been any change in the composition or behavior of our users in the past month?

Why it matters: Changes in user behavior or new user segments could affect model performance. Expected answer: We've seen an influx of new users from a different industry vertical. Impact on approach: We'd investigate if our models are adequately calibrated for these new use cases.