Studied Time series analysis, which is commonly used in fields such as finance and meteorology, involves examining data points ordered over time. By examining patterns, trends, and fluctuations in temporal data, this statistical method frequently provides insights into underlying behaviors. Finding trends, seasonal patterns, and autocorrelation are essential elements, and methods like decomposition and smoothing help reveal important information. In order to make predictions and identify anomalies, forecasting and anomaly detection are crucial components. Time series analysis helps to comprehend and utilize the temporal dependencies within data to make predictions and decisions by using techniques like Autoregressive Integrated Moving Average (ARIMA).