Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps

Takayuki Katsuki, Takayuki Osogami, Akira Koseki, Masaki Ono, Michiharu Kudo, Masaki Makino, Atsushi Suzuki

研究成果: Conference contribution

抄録

This paper proposes a method for modeling event sequences with ambiguous timestamps, a time-discounting convolution. Unlike in ordinary time series, time intervals are not constant, small time-shifts have no significant effect, and inputting timestamps or time durations into a model is not effective. The criteria that we require for the modeling are providing robustness against time-shifts or timestamps uncertainty as well as maintaining the essential capabilities of time-series models, i.e., forgetting meaningless past information and handling infinite sequences. The proposed method handles them with a convolutional mechanism across time with specific parameterizations, which efficiently represents the event dependencies in a time-shift invariant manner while discounting the effect of past events, and a dynamic pooling mechanism, which provides robustness against the uncertainty in timestamps and enhances the time-discounting capability by dynamically changing the pooling window size. In our learning algorithm, the decaying and dynamic pooling mechanisms play critical roles in handling infinite and variable length sequences. Numerical experiments on real-world event sequences with ambiguous timestamps and ordinary time series demonstrated the advantages of our method.

元の言語English
ホスト出版物のタイトル2018 IEEE International Conference on Data Mining, ICDM 2018
出版者Institute of Electrical and Electronics Engineers Inc.
ページ1085-1090
ページ数6
ISBN(電子版)9781538691588
DOI
出版物ステータスPublished - 27-12-2018
イベント18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore
継続期間: 17-11-201820-11-2018

出版物シリーズ

名前Proceedings - IEEE International Conference on Data Mining, ICDM
2018-November
ISSN(印刷物)1550-4786

Conference

Conference18th IEEE International Conference on Data Mining, ICDM 2018
Singapore
Singapore
期間17-11-1820-11-18

Fingerprint

Convolution
Time series
Parameterization
Learning algorithms
Experiments
Uncertainty

All Science Journal Classification (ASJC) codes

  • Engineering(all)

これを引用

Katsuki, T., Osogami, T., Koseki, A., Ono, M., Kudo, M., Makino, M., & Suzuki, A. (2018). Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps. : 2018 IEEE International Conference on Data Mining, ICDM 2018 (pp. 1085-1090). [8594948] (Proceedings - IEEE International Conference on Data Mining, ICDM; 巻数 2018-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2018.00139
Katsuki, Takayuki ; Osogami, Takayuki ; Koseki, Akira ; Ono, Masaki ; Kudo, Michiharu ; Makino, Masaki ; Suzuki, Atsushi. / Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps. 2018 IEEE International Conference on Data Mining, ICDM 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1085-1090 (Proceedings - IEEE International Conference on Data Mining, ICDM).
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abstract = "This paper proposes a method for modeling event sequences with ambiguous timestamps, a time-discounting convolution. Unlike in ordinary time series, time intervals are not constant, small time-shifts have no significant effect, and inputting timestamps or time durations into a model is not effective. The criteria that we require for the modeling are providing robustness against time-shifts or timestamps uncertainty as well as maintaining the essential capabilities of time-series models, i.e., forgetting meaningless past information and handling infinite sequences. The proposed method handles them with a convolutional mechanism across time with specific parameterizations, which efficiently represents the event dependencies in a time-shift invariant manner while discounting the effect of past events, and a dynamic pooling mechanism, which provides robustness against the uncertainty in timestamps and enhances the time-discounting capability by dynamically changing the pooling window size. In our learning algorithm, the decaying and dynamic pooling mechanisms play critical roles in handling infinite and variable length sequences. Numerical experiments on real-world event sequences with ambiguous timestamps and ordinary time series demonstrated the advantages of our method.",
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Katsuki, T, Osogami, T, Koseki, A, Ono, M, Kudo, M, Makino, M & Suzuki, A 2018, Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps. : 2018 IEEE International Conference on Data Mining, ICDM 2018., 8594948, Proceedings - IEEE International Conference on Data Mining, ICDM, 巻. 2018-November, Institute of Electrical and Electronics Engineers Inc., pp. 1085-1090, 18th IEEE International Conference on Data Mining, ICDM 2018, Singapore, Singapore, 17-11-18. https://doi.org/10.1109/ICDM.2018.00139

Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps. / Katsuki, Takayuki; Osogami, Takayuki; Koseki, Akira; Ono, Masaki; Kudo, Michiharu; Makino, Masaki; Suzuki, Atsushi.

2018 IEEE International Conference on Data Mining, ICDM 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1085-1090 8594948 (Proceedings - IEEE International Conference on Data Mining, ICDM; 巻 2018-November).

研究成果: Conference contribution

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AU - Suzuki, Atsushi

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N2 - This paper proposes a method for modeling event sequences with ambiguous timestamps, a time-discounting convolution. Unlike in ordinary time series, time intervals are not constant, small time-shifts have no significant effect, and inputting timestamps or time durations into a model is not effective. The criteria that we require for the modeling are providing robustness against time-shifts or timestamps uncertainty as well as maintaining the essential capabilities of time-series models, i.e., forgetting meaningless past information and handling infinite sequences. The proposed method handles them with a convolutional mechanism across time with specific parameterizations, which efficiently represents the event dependencies in a time-shift invariant manner while discounting the effect of past events, and a dynamic pooling mechanism, which provides robustness against the uncertainty in timestamps and enhances the time-discounting capability by dynamically changing the pooling window size. In our learning algorithm, the decaying and dynamic pooling mechanisms play critical roles in handling infinite and variable length sequences. Numerical experiments on real-world event sequences with ambiguous timestamps and ordinary time series demonstrated the advantages of our method.

AB - This paper proposes a method for modeling event sequences with ambiguous timestamps, a time-discounting convolution. Unlike in ordinary time series, time intervals are not constant, small time-shifts have no significant effect, and inputting timestamps or time durations into a model is not effective. The criteria that we require for the modeling are providing robustness against time-shifts or timestamps uncertainty as well as maintaining the essential capabilities of time-series models, i.e., forgetting meaningless past information and handling infinite sequences. The proposed method handles them with a convolutional mechanism across time with specific parameterizations, which efficiently represents the event dependencies in a time-shift invariant manner while discounting the effect of past events, and a dynamic pooling mechanism, which provides robustness against the uncertainty in timestamps and enhances the time-discounting capability by dynamically changing the pooling window size. In our learning algorithm, the decaying and dynamic pooling mechanisms play critical roles in handling infinite and variable length sequences. Numerical experiments on real-world event sequences with ambiguous timestamps and ordinary time series demonstrated the advantages of our method.

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Katsuki T, Osogami T, Koseki A, Ono M, Kudo M, Makino M その他. Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps. : 2018 IEEE International Conference on Data Mining, ICDM 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1085-1090. 8594948. (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2018.00139