Non-Linear Mining of Social Activities in Tensor Streams
Non-Linear Mining of Social Activities in Tensor Streams
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DOI：
10.1145/3394486.3403260
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发表时间：
2020-07
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通讯作者：
Koki Kawabata;Yasuko Matsubara;Takato Honda;Yasushi Sakurai
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文献类型：
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作者：
Koki Kawabata;Yasuko Matsubara;Takato Honda;Yasushi Sakurai
考虑到大量的时间演变事件序列，如谷歌网络搜索日志，这些事件是根据不同方面收集的，即时间戳、位置和关键字，我们如何准确地预测他们未来的活动？我们如何从如此复杂的张量流中揭示重要的模式，使我们能够进行长期预测？在本文中，我们提出了一种流方法，即CubeCast，该方法旨在捕捉张量流的基本趋势和季节性，并提取这些动态之间的时间和多维关系。我们提出的方法具有以下性质：(A)它是有效的：它同时发现趋势和季节性，并将它们的动态归结到同时的非线性潜在空间中。(B)它是自动的：它自动识别和模拟这种结构模式，而不需要任何参数调整或事先信息。(C)它是可伸缩的：它增量地和自适应地检测张量流的半无限集合的模式移动点。我们在真实数据集上进行的大量实验表明，该算法能够有效和高效地找到有意义的模式来生成未来值，并且在预测精度和计算时间方面优于最新的时间序列预测算法。
Given a large time-evolving event series such as Google web-search logs, which are collected according to various aspects, i.e., timestamps, locations and keywords, how accurately can we forecast their future activities? How can we reveal significant patterns that allow us to long-term forecast from such complex tensor streams? In this paper, we propose a streaming method, namely, CubeCast, that is designed to capture basic trends and seasonality in tensor streams and extract temporal and multi-dimensional relationships between such dynamics. Our proposed method has the following properties: (a) it is effective: it finds both trends and seasonality and summarizes their dynamics into simultaneous non-linear latent space. (b) it is automatic: it automatically recognizes and models such structural patterns without any parameter tuning or prior information. (c) it is scalable: it incrementally and adaptively detects shifting points of patterns for a semi-infinite collection of tensor streams. Extensive experiments that we conducted on real datasets demonstrate that our algorithm can effectively and efficiently find meaningful patterns for generating future values, and outperforms the state-of-the-art algorithms for time series forecasting in terms of forecasting accuracy and computational time.