TL-iTransformer: Revolutionizing sea surface temperature prediction through iTransformer and transfer learning

Abstract

The dynamics of Sea Surface Temperature (SST) are crucial for maintaining the balance of marine ecosystems. While existing artificial intelligence methods offer powerful tools for SST prediction, they struggle with data sparsity issues. To enhance SST prediction accuracy under sparse data conditions, this study proposes an innovative prediction model: TL-iTransformer. This model is based on the iTransformer architecture and incorporates transfer learning techniques specifically tailored for SST prediction. We begin by extracting SST features from data-rich sea areas (source sea areas) using a transfer learning strategy, integrating these features into the iTransformer model for pre-training. This process imparts the model with a priori knowledge and basic prediction capabilities, enabling it to adapt to data-sparse sea areas (target sea areas). The model is then fine-tuned using domain adaptive techniques to accurately capture the data characteristics and distribution patterns of the target sea area. We conducted a series of experiments using a real SST dataset from the sea area of British Columbia, Canada. The results demonstrate that TL-iTransformer maintains the Mean Absolute Error (MAE) and Mean Squared Error (MSE) within 0.144 and 0.356, respectively, under data sparsity conditions. Additionally, it outperforms four mainstream time-series prediction baseline models as the prediction time span increases. The proposed model can effectively address the issue of SST prediction in situations with sparse data.