Moving averages in ML Python - Model Metrics & Evaluation

Metrics & Evaluation - Moving averages

Which metric matters for Moving Averages and WHY

Moving averages smooth data to show trends clearly. The key metric is Mean Squared Error (MSE) or Mean Absolute Error (MAE) between the smoothed values and the original data. These metrics tell us how close the moving average is to the actual data points.

Lower error means the moving average follows the data well without too much noise. This helps in forecasting and spotting trends.

Confusion Matrix or Equivalent Visualization

Moving averages do not use confusion matrices because they are not classification models. Instead, we visualize the smoothing effect by plotting:

Original Data:  10, 12, 15, 14, 13, 16, 18, 20
Moving Average:  - , 12.33, 13.67, 14, 14, 14.33, 15.67, 18

This shows how the moving average smooths sudden changes.

Tradeoff: Smoothness vs Responsiveness

Using a longer window in moving averages smooths data more but reacts slower to changes. A shorter window reacts quickly but can be noisy.

Example:

• Short window (3 points): Captures quick changes but may show false ups and downs.
• Long window (7 points): Shows overall trend but misses sudden shifts.

Choosing the right window balances smoothness and responsiveness based on your goal.

Good vs Bad Metric Values for Moving Averages

Good: Low MSE or MAE means the moving average closely follows the data trend without too much noise.

Bad: High error means the moving average either misses important changes (too smooth) or is too noisy (too responsive).

For example, an MAE near zero is excellent, while a large MAE compared to data range means poor smoothing.

Common Pitfalls in Moving Average Metrics

• Ignoring window size: Using a window too large or too small can mislead interpretation.
• Over-smoothing: Losing important data patterns by averaging too much.
• Under-smoothing: Not reducing noise enough, leading to confusing trends.
• Comparing errors without context: Error size depends on data scale; always compare relative to data range.

Self Check

Your moving average model has a low MSE but reacts very slowly to sudden changes. Is it good?

Answer: It depends on your goal. If you want to see overall trends, this is good. But if you need to catch quick changes, it is not good because it is too smooth and slow.

Key Result

Mean Squared Error (MSE) or Mean Absolute Error (MAE) measure how well moving averages smooth data by balancing noise reduction and trend responsiveness.