A machine learning (ML) specialist at a retail company is forecasting sales for one of the company's stores. The ML specialist is using data from the past 10 years. The company has provided a dataset that includes the total amount of money in sales each day for the store. Approximately 5% of the days are missing sales data.
The ML specialist builds a simple forecasting model with the dataset and discovers that the model performs poorly. The performance is poor around the time of seasonal events, when the model consistently predicts sales figures that are too low or too high.
Which actions should the ML specialist take to try to improve the model's performance? (Choose two.)

A. Add information about the store's sales periods to the dataset.
B. Aggregate sales figures from stores in the same proximity.
C. Apply smoothing to correct for seasonal variation.
D. Change the forecast frequency from daily to weekly.
E. Replace missing values in the dataset by using linear interpolation.