Data pixelization for predicting completion time of events

Kamal, Imam Mustafa; Bae, Hyerim; Utama, Nur Ichsan; Choi, Yongki

doi:10.1016/j.neucom.2019.09.061

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…In this paper, the number of aircraft types is used as an input feature to capture the dynamic performance modes in various flight phases from many historical trajectories. In contrast to other features, the type of aircraft is a character attribute, which is coded via the one-hot encoding method [35].…”

Section: ) Basic Attributesmentioning

confidence: 99%

A Deep Learning Approach for Aircraft Trajectory Prediction in Terminal Airspace

et al. 2020

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Current state-of-the-art trajectory methods do not perform well in the terminal airspace that surrounds an airport due to its complex airspace structure and the frequently changing flight postures of aircraft. Since an aircraft that takes off or lands in an airport must follow a specified procedure, this paper will learn a data-driven trajectory prediction model from many historical trajectories to improve the accuracy and robustness of trajectory prediction in the terminal airspace. A regularization method is utilized to reconstruct each aircraft trajectory to obtain a high-quality trajectory with equal time intervals and no noise. Furthermore, we formulate the 4D trajectory prediction problem as a sequence-to-sequence learning problem, and we propose a sequence-to-sequence deep long short-term memory network (SS-DLSTM) for trajectory prediction, which can effectively capture the long and short temporal dependencies and the repetitive nature among trajectories. The proposed model is composed of an encoding module and a decoding module, where the encoding mode realizes the feature representation of historical trajectories, while the decoding module accepts the output of the encoding module as its initial input and recursively outputs the predicted trajectory sequence. The proposed method is applied to a dataset for the terminal airspace in Guangzhou, China. The experimental results demonstrate that our approach has relatively high robustness and outperforms mainstream data-driven trajectory prediction methods in terms of accuracy.

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Section: ) Basic Attributesmentioning

confidence: 99%

A Deep Learning Approach for Aircraft Trajectory Prediction in Terminal Airspace

et al. 2020

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“…It also differs from the imagery representation introduced in [15] that represents full traces populated with partially labeled events and is used to predict the completion time of a target event within a full trace by accounting for attributes of all events in the trace and completion times of events preceding the target one. Conversely, the imagery representation adopted in JARVIS is used to predict the next-activity of the running trace without having any information on the next event.…”

Section: Introductionmentioning

confidence: 99%

JARVIS: Joining Adversarial Training With Vision Transformers in Next-Activity Prediction

Pasquadibisceglie,

Appice,

Castellano

et al. 2024

IEEE Trans. Serv. Comput.

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In this paper, we propose a novel predictive process monitoring approach, named JARVIS, that is designed to achieve a balance between accuracy and explainability in the task of next-activity prediction. To this aim, JARVIS represents different process executions (traces) as patches of an image and uses this patch-based representation within a multi-view learning scheme combined with Vision Transformers (ViTs). Using multi-view learning we guarantee good accuracy by leveraging the variety of information recorded in event logs as different patches of an image. The use of ViTs enables the integration of explainable elements directly into the framework of a predictive process model trained to forecast the next trace activity from the completed events in a running trace by utilizing self-attention modules that give paired attention values between two picture patches. Attention modules disclose explainable information concerning views of the business process and events of the trace that influenced the prediction. In addition, we explore the effect of ViT adversarial training to mitigate overfitting and improve the accuracy and robustness of predictive process monitoring. Experiments with various benchmark event logs prove the accuracy of JARVIS compared to several current state-of-the-art methods and draw insights from explanations recovered through the attention modules.

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Causal Reasoning over Control-Flow Decisions in Process Models

Leemans

Tax

2022

Lecture Notes in Computer Science

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Data pixelization for predicting completion time of events

Cited by 4 publications

References 40 publications

A Deep Learning Approach for Aircraft Trajectory Prediction in Terminal Airspace

A Deep Learning Approach for Aircraft Trajectory Prediction in Terminal Airspace

JARVIS: Joining Adversarial Training With Vision Transformers in Next-Activity Prediction

Causal Reasoning over Control-Flow Decisions in Process Models

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