A retail chain wants to predict next month's sales based on historical data and seasonal trends. Which of the following methods is the most suitable for this task?

Time series analysis is ideal for predicting future values based on historical data and trends. It considers patterns such as seasonality and cyclicality, which are critical for sales forecasting. Methods like ARIMA or exponential smoothing are commonly used in this context. Option B : Regression analysis is versatile but less effective for capturing seasonality. Option C : Decision trees are better suited for classification than forecasting trends. Option D : Neural networks can model complex relationships but require extensive data preprocessing and may overfit for small datasets. Option E : A/B testing is unrelated to forecasting and is used for experimental analysis.