DIGWO-N-BEATS: An evolutionary time series prediction method for situation prediction

Hao Lin

Abstract:

Situation awareness is a key technology in many decision systems, but its performance bottleneck
in the third step, namely situation prediction, has not been overcome yet. This step involves
time series prediction and commonly uses deep learning methods. However, there are significant
performance differences among these methods, and the tuning of their hyperparameters has not
been thoroughly investigated. To address these challenges, a novel prediction method named
Distributed Improved Gray Wolf Optimizer-Neural Basis Expansion Analysis for Time-Series
(DIGWO-N-BEATS) is proposed for situation prediction tasks. First, an architecture based on N-BEATS, which is a cutting-edge paradigm, is formulated for modeling situation value time series.
Second, a novel improved evolutionary algorithm, which can converge in parallel, is proposed to
optimize thirteen hyperparameters and the model structures of N-BEATS. The experiment results
demonstrate that DIGWO-N-BEATS outperforms the six most competitive baselines, reducing the
average MAPE on two real-world situation awareness datasets and two time-series prediction
datasets by 8.18%, 1.12%, 9.92%, and 4.98%, respectively. Furthermore, DIGWO-N-BEATS
exhibits good convergence in hyperparameter optimization tasks and scalability.