Which model is primarily used for data that shows clear trends and seasons?

The time series model is specifically designed for data that exhibits patterns over time, including trends and seasonal variations. This model analyzes historical data points collected at consistent intervals, making it ideal for forecasting future values based on recognized patterns.

Time series analysis allows for the identification of long-term trends (persistent upward or downward movements in the data) as well as seasonal cycles (regular fluctuations that occur at specific periods, such as monthly or quarterly). By using historical data to inform predictions, the time series model can effectively capture these patterns and generate accurate forecasts.

The other options serve different purposes. For instance, a moving average smooths out fluctuations in the data to highlight longer-term trends but does not explicitly account for seasonality. Exponential smoothing gives more weight to recent observations for forecasting but also lacks a systematic approach to address trends and seasonality effectively. A causal model, on the other hand, is based on relationships between variables and would be used primarily when trying to establish cause-and-effect rather than analyzing the pattern and trends of a single data set over time.