Lag order = 3
时间: 2024-06-15 11:07:14 浏览: 22
Lag order, or simply lag, refers to the number of past ob used to predict future values in a time series analysis. In the context of autoregressive models, such as AR(p) models, the lag order determines the number of lagged terms included in the model.
When the lag order is set to 3, it means that three previous observations are used to predict the current value. This implies that the model takes into account the values at time t-1, t-2, and t-3 to forecast the value at time t.
By including multiple lagged terms in the model, we can capture the temporal dependencies and patterns in the data. The appropriate choice of lag order depends on the specific time series being analyzed and can be determined using various statistical techniques, such as autocorrelation function (ACF) or partial autocorrelation function (PACF) plots.
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