Moving averages in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Simple Moving Average (SMA)

Which of the following best describes the Simple Moving Average (SMA) in time series data?

A It calculates the average of all past data points up to the current point.

B It calculates the average of a fixed number of the most recent data points.

C It weights recent data points more heavily than older ones.

D It predicts future values by fitting a linear trend to the data.

💡 Hint

Think about how SMA uses a window of data points.

The Simple Moving Average calculates the average of a fixed number of recent data points, smoothing short-term fluctuations.

❓ Predict Output

intermediate

Output of Exponential Moving Average Calculation

What is the output of the following Python code that calculates an exponential moving average (EMA) with alpha=0.5 on the data [10, 20, 30]?

ML Python

data = [10, 20, 30]
alpha = 0.5
ema = [data[0]]
for x in data[1:]:
    ema.append(alpha * x + (1 - alpha) * ema[-1])
print([round(v, 2) for v in ema])

A [10, 15.0, 22.5]

B [10, 20.0, 30.0]

C [10, 15.0, 25.0]

D [10, 17.5, 23.75]

💡 Hint

EMA updates by weighting the new value and previous EMA.

EMA starts at 10, then 0.5*20 + 0.5*10 = 15, then 0.5*30 + 0.5*15 = 22.5.

❓ Model Choice

advanced

Choosing Moving Average Type for Noisy Data

You have a noisy sensor data stream and want to smooth it while giving more importance to recent readings. Which moving average method is best?

A Exponential Moving Average (EMA)

B Cumulative Moving Average (CMA)

C Simple Moving Average (SMA)

D Weighted Moving Average with equal weights

💡 Hint

Consider which method weights recent data more.

EMA weights recent data more heavily, making it better for noisy data where recent changes matter.

❓ Hyperparameter

advanced

Effect of Alpha in Exponential Moving Average

In an Exponential Moving Average (EMA), what is the effect of increasing the smoothing factor alpha from 0.1 to 0.9?

A The EMA becomes smoother and reacts slower to recent changes.

B The EMA becomes equivalent to a Simple Moving Average.

C The EMA ignores recent data and focuses on older data.

D The EMA becomes less smooth and reacts faster to recent changes.

💡 Hint

Alpha controls the weight of the newest data point.

Higher alpha means more weight on recent data, so EMA reacts faster but is less smooth.

❓ Metrics

expert

Comparing Moving Average Smoothing with MSE

You apply two smoothing methods on a noisy time series: SMA with window size 3 and EMA with alpha=0.3. Which metric best compares their smoothing quality against the original clean signal?

A Sum of absolute differences between noisy and smoothed signals

B Accuracy score of smoothed signal classification

C Mean Squared Error (MSE) between smoothed and clean signals

D Number of data points in the smoothed signal

💡 Hint

Think about measuring how close the smoothed signal is to the true clean signal.

MSE measures average squared difference between smoothed and clean signals, quantifying smoothing quality.