A time series is a sequence of observations recorded at successive points in time, typically at uniform intervals such as daily, monthly, quarterly, or yearly. Time series analysis is a crucial aspect of statistics, especially in economics, finance, and business, as it helps in understanding patterns, trends, and fluctuations in data over time. The primary objective of analyzing a time series is to identify underlying patterns and use them to forecast future values, which is essential for planning and decision-making in various economic contexts. Time series data differs from cross-sectional data in that it emphasizes temporal order, making the sequence of observations significant. The analysis of time series involves detecting systematic patterns and isolating random variations to gain meaningful insights. Time series can be broadly divided into components that explain its behavior. These components help in modeling and forecasting by separating the underlying causes of variation in the data. The main components of a time series are trend, seasonal, cyclical, and irregular components. The trend component represents the long-term movement or direction of the data. It indicates whether the data is generally increasing, decreasing, or remaining constant over a prolonged period. Identifying the trend is crucial for understanding the overall growth or decline in economic indicators, such as GDP, inflation, or sales revenue. Trends may be linear, exponential, or follow other mathematical forms, and recognizing them allows economists and analysts to distinguish between short-term fluctuations and long-term tendencies. The seasonal component captures regular and repetitive fluctuations that occur within a