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Dynamic Modeling and Forecasting of Time-evolving Data Streams

Published: 25 July 2019 Publication History

Abstract

Given a large, semi-infinite collection of co-evolving data sequences (e.g., IoT/sensor streams), which contains multiple distinct dynamic time-series patterns, our aim is to incrementally monitor current dynamic patterns and forecast future behavior. We present an intuitive model, namely OrbitMap, which provides a good summary of time-series evolution in streams. We also propose a scalable and effective algorithm for fitting and forecasting time-series data streams. Our method is designed as a dynamic, interactive and flexible system, and is based on latent non-linear differential equations. Our proposed method has the following advantages: (a) It is effective: it captures important time-evolving patterns in data streams and enables real-time, long-range forecasting; (b) It is general: our model is general and practical and can be applied to various types of time-evolving data streams; (c) It is scalable: our algorithm does not depend on data size, and thus is applicable to very large sequences. Extensive experiments on real datasets demonstrate that OrbitMap makes long-range forecasts, and consistently outperforms the best existing state-of-the-art methods as regards accuracy and execution speed.

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    cover image ACM Conferences
    KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
    July 2019
    3305 pages
    ISBN:9781450362016
    DOI:10.1145/3292500
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    Published: 25 July 2019

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    Author Tags

    1. data stream
    2. forecasting
    3. non-linear systems
    4. time series

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    • MIC/SCOPE

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    KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;
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    • (2024)Kernel Representation Learning with Dynamic Regime Discovery for Time Series ForecastingAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2266-2_20(251-263)Online publication date: 25-Apr-2024
    • (2023)Modeling Regime Shifts in Multiple Time SeriesACM Transactions on Knowledge Discovery from Data10.1145/359285717:8(1-31)Online publication date: 28-Jun-2023
    • (2022)Meta-learning dynamics forecasting using task inferenceProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601843(21640-21653)Online publication date: 28-Nov-2022
    • (2022)Fast Mining and Forecasting of Co-evolving Epidemiological Data StreamsProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539078(3157-3167)Online publication date: 14-Aug-2022
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    • (2020)GLIMA: Global and Local Time Series Imputation with Multi-directional Attention Learning2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378408(798-807)Online publication date: 10-Dec-2020
    • (2019)Automatic Sequential Pattern Mining in Data StreamsProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3358002(1733-1742)Online publication date: 3-Nov-2019
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