A machine learning (ML) specialist at a retail company must build a system to forecast the daily sales for one of the company's stores. The company provided the ML specialist with sales data for this

Aggregating sales from other stores might introduce irrelevant patterns or noise, potentially making the model less accurate for forecasting sales specifically for the target store.

Applying smoothing for seasonal variation is a valid time series technique, but it does not address the fundamental issue of 10% missing data, which is a more critical problem impacting model performance.

Changing the forecast frequency to weekly reduces granularity and does not directly resolve the problem of missing daily data, which can still affect weekly aggregates or the ability to forecast at a desired frequency.

Missing values in time series data disrupt continuity and hinder a model's ability to learn accurate patterns and trends, leading to poor forecasting performance. Replacing these missing sales values using linear interpolation or other imputation methods creates a complete dataset, which is crucial for the model to effectively capture the underlying temporal dynamics and significantly improve its accuracy.