Time series analysis is a statistical method used to understand and interpret data collected sequentially over time. It involves examining trends, patterns, and relationships within the data to predict future outcomes or understand underlying patterns.
This analysis employs various techniques, including descriptive statistics to summarize data trends, smoothing methods to reduce noise, forecasting models to predict future values, decomposition to identify underlying components, and correlation analysis to understand relationships between different time periods.
By leveraging historical data, time series analysis enables predictions and informed decision-making across different fields such as finance, economics, weather forecasting, and more, aiding in planning and strategy formulation based on past trends and patterns.