What type of analysis typically involves identifying trends, cycles, and seasonal patterns?

Time Series Analysis is specifically designed to identify trends, cycles, and seasonal patterns in data over time. This type of analysis focuses on a sequence of data points collected or recorded at specific time intervals. By analyzing these data points, analysts can observe how values change over time, allowing for the detection of consistent patterns and the forecasting of future trends based on historical data.

In practice, Time Series Analysis is commonly applied in various fields, including economics, finance, and environmental studies, where understanding fluctuations in data is critical for decision-making. By recognizing trends, analysts can determine long-term changes in data, while cycles may indicate periodic fluctuations, and seasonal patterns capture phenomena that recur at regular intervals throughout the year.

Descriptive Analysis, on the other hand, summarizes past data but does not focus specifically on temporal trends, cycles, or seasonal variations. Comparative Analysis examines relationships between different data sets rather than examining data changes over time. Causal Analysis seeks to identify cause-and-effect relationships, which may be more focused on variables influencing one another rather than observing their behavior over time.