ITS-Mina introduces an all-MLP model with iterative refinement, external attention via learnable memory units, and HHO-tuned dropout that reports state-of-the-art or competitive results on six multivariate time series benchmarks versus eleven baselines.
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ITS-Mina: A Harris Hawks Optimization-Based All-MLP Framework with Iterative Refinement and External Attention for Multivariate Time Series Forecasting
ITS-Mina introduces an all-MLP model with iterative refinement, external attention via learnable memory units, and HHO-tuned dropout that reports state-of-the-art or competitive results on six multivariate time series benchmarks versus eleven baselines.