A Granule-Based Time Series Forecasting (TSF) Model for Underwater Combat with Missing Data

Abstract

Abstract The observed data for underwater combat unit are time series data but with high uncertainty. In this paper, we develop a time series forecasting model with granular input space. Missing values are efficiently formed into granular space under the specificity and coverage criteria, and the time series matrix is fed to a model with 4-layer Long Short Term Memory (LSTM) and self-attention. We explore an efficient way of combining granular input space and LSTM-based structure. Both the criteria of forming granular input space and time series data representation are pre-defined. To avoid forgetting the previous information, self-attention mechanism is used for granular samples at the same time. We use data from a computer simulation case as well as public data sets to test our model, and experiments demonstrate that our granular data matrix with self-attention can boost the performance for movement forecasting with high accuracy. Granular input matrix is proved effective when we augment our granular LSTM-based structure.KeywordsGranular spaceMissing valuesSelf-attentionLSTMTime series forecasting