Clear Explanations of AR, MA, ARMA, and ARIMA in Times Series Analysis

Time series analysis is widely used for stock market analysis, sales forecasting, economic forecasting, astronomy, sales forecasting, weather forecasting, medical devices to monitor vitals in real time, etc.

In time series analysis, the most highly used models are AR(Autoregressive), MA(Moving Average), ARMA(Autoregressive Moving Average), and ARIMA (Autoregressive Integrated Moving Average). These models are used for forecasting the future data using the available historical data.

Autoregressive Model (AR)

Autoregressive model represents a regression or prediction of current value that is generated based on the past values from that same time series. Since the current value is based on the nearby previous values, the value doesn’t change instantaneously. For example, body temperature doesn’t go up and down rapidly. It changes steadily and the measurements are close to each other.

Moving Average Model (MA)

Before we discuss moving average models, let’s not get confused with moving average smoothing (that smoothes out the times series by averaging) with moving average model.

Moving average model (MA) model generates the current values based on the ERRORS from the past forecasts instead of using the past values like AR. Past errors are analyzed to produce the current value. Perfecting a baking recipe will be like a moving average model. You will do adjustments for…