Which forecasting algorithms utilize the SAP HANA Predictive Analysis Library?

The correct choice highlights algorithms that leverage the capabilities of the SAP HANA Predictive Analysis Library, which is designed for advanced analytical functions. Demand Sensing, Pre-Processing, and Best Model Selection are key components that benefit from robust predictive analytics.

Demand Sensing uses real-time data to adjust forecasts quickly, improving accuracy by recognizing patterns and trends. Pre-Processing refers to the steps taken to clean and organize data before analysis, ensuring that the information used for forecasting is reliable and relevant. Best Model Selection involves choosing the most appropriate forecasting model among various options based on historical data and specific business criteria.

The other options, while related to forecasting and analysis, do not directly utilize the SAP HANA Predictive Analysis Library in the same way. For instance, Market Analysis and Customer Insights focus more on broader strategic evaluations rather than specific algorithmic forecasting techniques. Similarly, Statistical Outlier Detection and Time-Series Analysis can be part of data analysis but don't represent the particular set of algorithms that exploit SAP HANA's predictive features as effectively. Lastly, Basic Trend Analysis and Inventory Optimization are operational-level practices that do not inherently involve the predictive algorithms offered by SAP HANA.