Data imputation in large and small-scale spatiotemporal time series gaps using BackForward Bi-LSTM

Data imputation in large and small-scale spatiotemporal time series gaps using BackForward Bi-LSTM

Abstract In recent years, tracking systems have transformed the understanding of spatiotemporal dynamic processes. However, these systems often face challenges due to missing data caused by technical limitations or intentional manipulation, leading to classification bias and hidden suspicious activi...

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Bibliographic Details
Main Authors: Alessandro Galdelli, Gagan Narang, Selene Tomassini, Lorenzo D’Agostino, Anna Nora Tassetti, Adriano Mancini
Format: Article
Language:English
Published: SpringerOpen 2025-05-01
Series:Journal of Big Data
Subjects:
Time series
Deep learning
Neural networks
Trajectory imputation
AIS
Online Access:https://doi.org/10.1186/s40537-025-01163-0
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https://doi.org/10.1186/s40537-025-01163-0

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