PDE Preparing and Using Data for Analysis Practice Question
You need to create a time-series forecast for inventory demand using BigQuery ML. The data includes daily sales for 5 years. Which model type should you use?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
ARIMA_PLUS
BigQuery ML supports ARIMA_PLUS for time-series forecasting. Linear regression, k-means, and matrix factorization are not appropriate for time-series forecasting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
K-means
Why it's wrong here
K-means partitions rows into unsupervised clusters based on feature similarity; it produces no predictive function and ignores the temporal ordering of daily sales. It is tempting because it is a common BigQuery ML model, but it suits segmentation tasks. ARIMA_PLUS handles trend and seasonality for time-series forecasting.
- ✗
Linear regression
Why it's wrong here
Linear regression fits a straight-line relationship between input features and a label, treating rows as independent rather than ordered in time. It is tempting because it forecasts a numeric value, but it cannot model trend, seasonality or autocorrelation. ARIMA_PLUS is the BigQuery ML time-series model for this data.
- ✓
ARIMA_PLUS
Why this is correct
ARIMA_PLUS handles seasonality, trends and holiday effects automatically, which suits five years of daily sales data containing weekly and yearly patterns. It also supports forecasting directly in BigQuery ML without exporting data, satisfying the requirement to build a time-series forecast for inventory demand.
- ✗
Matrix factorization
Why it's wrong here
Matrix factorization predicts ratings or recommendations from sparse user-item interactions, so it cannot model the trend and seasonality in five years of daily sales. It is tempting because it handles time-stamped interaction data, and it would be correct for product recommendation or demand segmentation, not forecasting.
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