Time Series Analysis

Overview

Time series analysis examines data points collected over time to identify patterns, trends, and seasonality for forecasting and understanding temporal dynamics.

When to Use

• Forecasting future values based on historical trends
• Detecting seasonality and cyclical patterns in data
• Analyzing trends over time in sales, stock prices, or website traffic
• Understanding autocorrelation and temporal dependencies
• Making time-based predictions with confidence intervals
• Decomposing data into trend, seasonal, and residual components

Core Components

• Trend: Long-term directional movement
• Seasonality: Repeating patterns at fixed intervals