Moving averages in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a moving average in data analysis?

A moving average is a way to smooth out data by taking the average of a fixed number of recent points. It helps show trends by reducing noise.

intermediate

Explain the difference between a simple moving average (SMA) and an exponential moving average (EMA).

SMA calculates the average of data points equally over a fixed window. EMA gives more weight to recent data points, making it respond faster to changes.

beginner

Why are moving averages useful in machine learning?

They help smooth noisy data, making patterns clearer. This can improve model training by focusing on trends rather than random fluctuations.

intermediate

How does the window size affect a moving average?

A larger window smooths data more but reacts slower to changes. A smaller window reacts faster but may keep more noise.

beginner

Write the formula for a simple moving average (SMA) of window size N.

SMA at time t = (x_t + x_{t-1} + ... + x_{t-N+1}) / N, where x_i are data points.

What does a moving average help to reduce in data?

A Data size

B Trends

C Noise

D Accuracy

Moving averages smooth data by reducing noise, making trends easier to see.

Which moving average type gives more weight to recent data?

A Exponential Moving Average (EMA)

B Cumulative Moving Average (CMA)

C Weighted Moving Average (WMA)

D Simple Moving Average (SMA)

EMA assigns more weight to recent points, reacting faster to changes.

If you want a moving average to react quickly to recent changes, you should use:

A A large window size

B No moving average

C Only past data

D A small window size

A smaller window size makes the moving average more sensitive to recent changes.

Which of these is NOT a benefit of using moving averages?

A Increasing data noise

B Highlighting trends

C Smoothing noisy data

D Helping model training

Moving averages reduce noise, not increase it.

The formula SMA at time t = (x_t + x_{t-1} + ... + x_{t-N+1}) / N means:

A Sum of all data points

B Average of last N data points

C Difference between data points

D Product of data points

SMA is the average of the last N data points.

Describe what a moving average is and why it is useful in analyzing data.

Think about how averaging recent values helps see the bigger picture.

Explain the difference between simple moving average and exponential moving average and when you might use each.

Consider how quickly you want the average to respond to new data.