1. Abstract
Highly volatile indicators can obscure meaningful patterns and weaken correlation quality. Applying appropriate smoothing techniques helps stabilize signals and ensures analysis reflects underlying trends rather than short-term noise.
2. Context
Use this best practice when using sentiment surveys, high-frequency operational metrics, or inherently noisy internal data prior to Discover or Predict.
3. Content
3.1 Why It Matters
Volatile data can produce misleading signals. Month-to-month swings may reflect reporting quirks, survey sentiment shifts, or short-term disruptions rather than structural change. When used unfiltered, these fluctuations can:
- Suppress true correlations
- Produce unstable coefficients
- Cause overreaction in forecasts
Smoothing does not remove meaningful movement; it clarifies it.
3.2 How to Apply
- Review the raw series visually to assess volatility.
- Apply a 3-period moving average (3MMA) or Year-over-Year transformation using the Transform Modal.
- Compare the smoothed series against the original to ensure inflection points remain intact.
- Validate that smoothing improves interpretability without masking structural change.
- Use the smoothed version consistently across analysis steps.
3.3 Example
A monthly consumer sentiment index shows sharp oscillations. After applying a 3MMA, the broader downward trend becomes visible and aligns more clearly with declining sales.
3.4 Common Pitfalls
- Over-smoothing and eliminating turning points
- Applying smoothing inconsistently across related indicators
- Failing to document transformations
3.5 Expected Results
- Clearer directional trends
- Improved correlation strength
- More stable model performance
