This research was conducted to compare time series data with the aim of understanding developments, patterns, and changes occurring within a given observation period. The analysis included observations of short-term and long-term trends, seasonal patterns, and the level of data volatility over time. The research data came from secondary sources and was analyzed using a quantitative statistical approach to identify differences in characteristics between the objects studied. The analysis results indicate that each object has a distinct growth pattern and level of fluctuation, influenced by various internal and external factors. The time series approach also allows for the identification of performance consistency and changes in data structure over the study period. Therefore, the results of this research can be used as a basis for future performance evaluation, planning, and forecasting.
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