What three data actions enhance forecast accuracy for a financial analyst in SAP Analytics Cloud?

The selection of time series forecasting, anomaly detection, and data cleansing as the three data actions to enhance forecast accuracy is well-founded in the context of financial analysis within SAP Analytics Cloud.

Time series forecasting is a critical technique used when analyzing financial data that is collected at regular intervals over time. It provides insights into trends, seasonality, and cyclical patterns, helping financial analysts to predict future values based on historical data. This is particularly useful in generating accurate forecasts regarding revenue, expenses, and other key financial metrics.

Anomaly detection is equally important, as it allows analysts to identify outliers or unusual patterns in the data. By recognizing these anomalies, analysts can investigate root causes and determine if they represent genuine insights or errors in the dataset. This capability ensures that forecasts remain credible and that significant fluctuations that could distort financial predictions are carefully addressed.

The inclusion of data cleansing strengthens the process further by ensuring that the data used for forecasting and analysis is accurate, consistent, and free from errors. Clean data is crucial for not only improving the reliability of forecasts but also for maintaining the integrity of the analysis.

Other options may present different combinations of actions, but they do not encompass the full range of enhancements that time series forecasting, anomaly detection, and data cleansing provide collectively