Long time series of ocean wave prediction based on PatchTST model

Abstract

In recent years, AI (artificial intelligence) algorithms based on big data have gradually emerged and been applied to short time predictions of ocean waves (e.g. 1 h, 3 h, …, 48 h). However, limited by the lack of a better method and time-consuming computation, long time (e.g. 720 h) predictions have not been investigated yet. Based on the problems with study of long time series wave prediction, by using the hourly wave reanalysis data near Bering Sea from 2016 to 2020, this study establishes a long time series AI prediction model for waves with a forecast length of one month (720 h) based on PatchTST model. The time series prediction results of significant wave height evolution in 2021 show that compared with Informer, LSTM (Long Short Term Memory) and NeuralProphet, PatchTST's is significantly superior with COR (Correlation) averages at 0.90, RMSE (Root Mean Square Error) averages at 0.65 and MAE (Mean Absolute Error) averages at 0.56 on 12 months of 2021, which can be effectively applied to long time significant wave height prediction.