Autoregressive models are an essential tool in analyzing time series data. They capture the relationship between an observation and a number of lagged observations, allowing us to predict future values based on past values.

Autoregressive models have various applications such as stock market prediction, climate forecasting, and traffic flow analysis.

Understanding Time Series Analysis

Time Series Data

Time series data is a sequence of observations recorded over time. It exhibits temporal dependence, where each value is dependent on previous values. Examples of time series data include stock prices, weather measurements, and website traffic.

Stationarity

Stationarity is a crucial assumption in time series analysis. A stationary time series has constant mean, variance, and autocovariance over time.