Example Concept: A common strategy for improving data quality in time series forecasting is to use interpolation methods to handle missing values. This involves estimating missing data points within a time series by using statistical techniques such as linear interpolation, spline interpolation, or more advanced methods like Kalman filtering. Additionally, outlier detection and removal can be employed to ensure that anomalies do not skew the forecasting model. Ensuring temporal consistency by aligning time zones and correcting for daylight saving time changes is also crucial for maintaining data integrity.