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Answer: Time series analysis to analyze sales data points collected at successive time intervals.
**Correct Option: C. Time series analysis** Time series analysis is specifically designed for analyzing data points collected or recorded at successive time intervals, making it the most appropriate technique for evaluating the consistency and reliability of sales data over time. It can identify underlying patterns, trends, and seasonal variations, as well as detect anomalies or inconsistencies in the data. **Why other options are incorrect:** - **A. Cluster analysis**: This technique groups similar data points together based on certain criteria but does not account for the temporal aspect of the data, making it unsuitable for evaluating data over time. - **B. Data shuffling**: This process involves randomly rearranging data to prevent bias during model training but does not provide any insights into data reliability or consistency over time. - **D. Cross-validation**: While useful for assessing model performance, cross-validation does not directly evaluate the reliability or consistency of data over time.
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In the context of a large-scale e-commerce platform, you are tasked with evaluating the consistency and reliability of sales data across different time periods to identify trends, seasonal effects, and anomalies. The platform handles millions of transactions daily, and the data is stored in a time-series database. Given the need for accuracy and the volume of data, which of the following techniques is MOST appropriate for this task? Choose one correct option.
A
Cluster analysis to group similar sales patterns together without considering time.
B
Data shuffling to randomly rearrange sales data to prevent model bias.
C
Time series analysis to analyze sales data points collected at successive time intervals.
D
Cross-validation to assess how well a predictive model generalizes to an independent dataset.
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