Evaluating ARIMA model performance with cross-validation techniques
Time series forecasting is a critical aspect of many fields, including finance, economics, and meteorology. One of the most widely used models for time series forecasting is the ARIMA (AutoRegressive Integrated Moving Average) model.
Evaluating the performance of ARIMA models accurately is essential for making reliable predictions, especially when dealing with climatic data. Cross-validation, a robust statistical technique, plays a significant role in this evaluation process.
This article delves into the importance of ARIMA models, the benefits and limitations of using cross-validation for evaluating these models, and how statistical methods underpin the effectiveness of this approach.
