Autoregressive Drift is a concept in time series analysis and forecasting that describes a situation where the predicted values from a model tend to drift away from the actual values over time. This drift can occur in autoregressive models, which are statistical models that use previous values in a series to predict future values. The tendency for predictions to diverge from reality can be attributed to several factors, including model mis-specification, changes in underlying data patterns, or external influences that were not accounted for in the model.
The autoregressive component of the model indicates that the current value is based on its own previous values, but if the model fails to adapt to new trends or shifts in the data, the forecasts can become increasingly inaccurate. For example, in economic forecasting, if a model is trained on data from a stable economic period, it may not perform well during a recession or recovery, leading to a noticeable drift in predictions.
Mitigating autoregressive drift often involves regularly updating the model with new data, incorporating additional explanatory variables, or using more sophisticated techniques like dynamic models that can adjust to changes in the data over time. Understanding and addressing autoregressive drift is crucial for improving the accuracy of time series forecasts and making better-informed decisions based on these predictions.