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    We developed a new algorithm for spatiotemporal prediction using point processes to accurately forecast events in nonstationary environments. This method improves predictions for both dense and sparse data, outperforming existing deep learning techniques.

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    Area of Science:

    • Machine Learning
    • Statistical Modeling
    • Data Science

    Background:

    • Spatiotemporal prediction is crucial for real-world applications like crime and earthquake forecasting.
    • Existing methods often struggle with nonstationary data and varying spatial densities.
    • The inherent complexities of event prediction domains require novel approaches.

    Purpose of the Study:

    • To introduce a novel point-process-based algorithm for nonstationary spatiotemporal prediction.
    • To address challenges in predicting events in both densely and sparsely distributed sequences.
    • To develop a probabilistic model capable of learning spatial partitioning and inter-region interactions.

    Main Methods:

    • Developed a probabilistic approach partitioning the spatial domain into subregions.
    • Modeled event arrivals within each region using interacting point processes.
    • Employed a gradient-based optimization procedure for joint learning of spatial partitioning and interactions.

    Main Results:

    • Achieved significant performance improvements over baseline and state-of-the-art deep learning methods.
    • Demonstrated effectiveness on both simulated and two real-life datasets.
    • Provided empirical results showing parameter effects and guidance for parameter selection.

    Conclusions:

    • The proposed point-process-based algorithm offers superior performance for nonstationary spatiotemporal prediction.
    • The method effectively handles varying data densities and learns complex spatial interactions.
    • This work advances the field of event prediction with a robust and adaptable probabilistic framework.