Time series analysis is an important method for understanding time data and includes components such as identifying trends, identifying recurring patterns (seasonality), and observing long-term wave movements (cyclical patterns). Smoothing techniques such as moving averages and exponential smoothing improve analysis by highlighting trends. Decomposition breaks the data into trends, seasonality and residual components for clarity. Ensuring stationarity, where statistical properties remain constant, often requires differentiation or transformations. The autocorrelation and partial autocorrelation functions identify dependencies and relationships between observations at different lags. Forecasting methods are central, and ARIMA models combine autoregressive, differential, and moving average components. Exponential smoothing methods contribute to accurate forecasts, and advanced models such as Prophet and Long Short-Term Memory (LSTM) improve forecasting capabilities. Applications of time series analysis include financial forecasting, demand forecasting for inventory management, and energy consumption optimization. Overall, time series analysis provides a comprehensive framework for gaining insights, making informed decisions, and accurately predicting trends in various time series data.