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Question 294 - MLS-C01 discussion

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A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this store from the past 10 years. The historical dataset includes the total amount of sales on each day for the store. Approximately 10% of the days in the historical dataset are missing sales data.

The ML specialist builds a forecasting model based on the historical dataset. The specialist discovers that the model does not meet the performance standards that the company requires.

Which action will MOST likely improve the performance for the forecasting model?

A.

Aggregate sales from stores in the same geographic area.

Answers
A.

Aggregate sales from stores in the same geographic area.

B.

Apply smoothing to correct for seasonal variation.

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B.

Apply smoothing to correct for seasonal variation.

C.

Change the forecast frequency from daily to weekly.

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C.

Change the forecast frequency from daily to weekly.

D.

Replace missing values in the dataset by using linear interpolation.

Answers
D.

Replace missing values in the dataset by using linear interpolation.

Suggested answer: D

Explanation:

When forecasting sales data, missing values can significantly impact model accuracy, especially for time series models. Approximately 10% of the days in this dataset lack sales data, which may cause gaps in patterns and disrupt seasonal trends. Linear interpolation is an effective technique for estimating and filling in missing data points based on adjacent known values, thus preserving the continuity of the time series.

By interpolating the missing values, the ML specialist can provide the model with a more complete and consistent dataset, potentially enhancing performance. This approach maintains the daily data granularity, which is important for accurately capturing trends at that frequency.

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asked 31/10/2024
Samya Sharab
40 questions
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