Time Series Forecasting via an Elastic Optimal Adaptive GM(1,1) Model

The GM(1,1) model is a well-established approach for time series forecasting, demonstrating superior effectiveness with limited data and incomplete information. However, its performance often degrades in dynamic systems, leading to obvious prediction errors. To address this impediment, we propose an elastic optimal adaptive GM(1,1) model, dubbed EOAGM, to improve forecasting performance. Specifically, our proposed EOAGM dynamically optimizes the sequence length by discarding outdated data and incorporating new data, reducing the influence of irrelevant historical information. Moreover, we introduce a stationarity test mechanism to identify and adjust sequence data fluctuations, ensuring stability and robustness against volatility. Additionally, the model refines parameter optimization by incorporating predicted values into candidate sequences and assessing their impact on subsequent forecasts, particularly under conditions of data fluctuation or anomalies. Experimental evaluations across multiple real-world datasets demonstrate the superior prediction accuracy and reliability of our model compared to six baseline approaches.