Choosing between parametric and non-parametric time series models hinges primarily on your data characteristics and the goals of your analysis. Parametric models, such as ARIMA (AutoRegressive Integrated Moving Average), rely on specific assumptions about the underlying processes that generate the data, including fixed parameters such as trend and seasonality. These models are often easier to interpret and can provide robust forecasts when the assumptions hold true. If you have a strong understanding of the underlying patterns in your data and they can be captured with a finite set of parameters, a parametric model may be appropriate.
On the other hand, non-parametric models, such as kernel regression or nearest neighbors, do not rely on a predefined structure and can adapt to the data’s inherent properties. These models are useful when you suspect the relationships in your data may be complex or when the assumptions required for parametric models do not hold. For instance, if you are dealing with irregularly spaced time data or experience nonlinear patterns that aren’t easily captured by standard models like ARIMA, non-parametric approaches can offer greater flexibility and potentially more accurate forecasts.
Ultimately, the choice also depends on computational efficiency and interpretability. Parametric models tend to require less data to produce reliable estimates, making them quicker to run, which is an important aspect when working with large datasets. Non-parametric methods, while often more accurate for complex structures, typically require more data and longer computation times. Therefore, consider both the nature of your data and your specific forecasting needs when deciding on the best approach.