Time Series Analysis

Time series analysis is a vital statistical technique for examining data points collected or recorded at time intervals. In R, it involves identifying patterns, trends, seasonality, and cyclical behavior within a dataset. A typical time series analysis begins with data visualization to understand underlying trends, followed by decomposition to separate the data into trend, seasonal, and residual components. Ensuring stationarity is crucial, as non-stationary data can mislead results; this is often checked using the Augmented Dickey-Fuller (ADF) test.