What is the purpose of data cleansing before generating a statistical forecast?

The purpose of data cleansing before generating a statistical forecast is to ensure that the historical data used is free of errors and anomalies. Clean data is critical for accurate statistical analysis because any inaccuracies can lead to misleading forecasts. Errors or anomalies in the data, such as outliers, duplicates, or incorrect entries, can distort the relationships and patterns that statistical models rely on to predict future outcomes.

By cleansing the data, analysts can remove or correct these inaccuracies, ensuring that the dataset reflects a true and reliable representation of past performance. This refined data becomes the foundation for generating forecasts that are both credible and actionable. Essentially, data cleansing is a crucial step in the forecasting process, as the quality of input data directly impacts the quality of the forecast results.