Improved Hierarchical Temporal Memory for Online Prediction of Ocean Time Series Data

Abstract

Time series prediction is an effective tool for marine scientific research. The Hierarchical Temporal Memory (HTM) model has advantages over traditional recurrent neural network (RNN)-based models due to its online learning and prediction capabilities. Given that the neuronal structure of HTM is ill-equipped for the complexity of long-term marine time series applications, this study proposes a new, improved HTM model, incorporating Gated Recurrent Units (GRUs) neurons into the temporal memory algorithm to overcome this limitation. The capacities and advantages of the proposed model were tested and evaluated on time series data collected from the Xiaoqushan Seafloor Observatory in the East China Sea. The improved HTM model both outperforms the original one in short-term and long-term predictions and presents results with lower errors and better model stability than the GRU model, which is proficient in long-term predictions. The findings allow for the conclusion that the mechanism of online learning has certain advantages in predicting ocean observation data.

Keywords

• ocean observation
• time series prediction
• Gated Recurrent Units (GRUs)
• improved Hierarchical Temporal Memory (HTM)