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Efficient forecasting of ship order books holds immense significance in the maritime industry, enabling companies to optimize their operations, allocate resources effectively, and make informed decisions. However, volatile characteristics within historical order books pose challenges in achieving reliable, intelligent, and precise forecasts. This paper presents a novel ensemble deep random vector functional link (edRVFL) algorithm to anticipate future ship order book dynamics. The edRVFL leverages deep feature extraction and ensemble learning to enhance forecasting performance. To further elevate its capabilities, we introduce a discontinuous and parsimonious embedding strategy, which deviates from the conventional dense collection of continuous time steps used in vanilla edRVFL. This parsimonious embedding approach limits the model's complexity and boosts its generalization ability. We extensively evaluate the proposed method using ship order book data, and comparative studies demonstrate its superiority over alternative approaches. Our proposed edRVFL offers a promising solution for accurate and efficient ship order book forecasting, making it a valuable asset in the maritime industry's decision-making processes. The source codes utilized in this research are openly available on GitHub at the following link: https://github.com/crkkkaa/Ship-order-book-forecasting-by-an-ensemble-deep-parsimonious-random-vector-functional-link-network-. • A novel ensemble deep RVFL to anticipate ship order book dynamics is proposed. • We propose a parsimonious embedding algorithm to optimize temporal patterns. • The direct links ablation study is conducted for comparison. [ABSTRACT FROM AUTHOR]

Efficient forecasting of ship order books holds immense significance in the maritime industry, enabling companies to optimize their operations, allocate resources effectively, and make informed decisions. However, volatile characteristics within historical order books pose challenges in achieving reliable, intelligent, and precise forecasts. This paper presents a novel ensemble deep random vector functional link (edRVFL) algorithm to anticipate future ship order book dynamics. The edRVFL leverages deep feature extraction and ensemble learning to enhance forecasting performance. To further elevate its capabilities, we introduce a discontinuous and parsimonious embedding strategy, which deviates from the conventional dense collection of continuous time steps used in vanilla edRVFL. This parsimonious embedding approach limits the model's complexity and boosts its generalization ability. We extensively evaluate the proposed method using ship order book data, and comparative studies demonstrate its superiority over alternative approaches. Our proposed edRVFL offers a promising solution for accurate and efficient ship order book forecasting, making it a valuable asset in the maritime industry's decision-making processes. The source codes utilized in this research are openly available on GitHub at the following link: https://github.com/crkkkaa/Ship-order-book-forecasting-by-an-ensemble-deep-parsimonious-random-vector-functional-link-network-. • A novel ensemble deep RVFL to anticipate ship order book dynamics is proposed. • We propose a parsimonious embedding algorithm to optimize temporal patterns. • The direct links ablation study is conducted for comparison. [ABSTRACT FROM AUTHOR]