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Long-memory time series ensembles for concept shift detection

Published: 11 August 2013 Publication History

Abstract

Usually time series are controlled by generative processes which display changes over time. On many occasions, two or more generative processes may switch forcing the abrupt replacement of a fitted time series model by another one. We claim that the incorporation of past data can be useful in the presence of concept shift. We believe that history tends to repeat itself and from time to time, it is desirable to discard recent data reusing old past data to perform model fitting and forecasting. We address this challenge by introducing an ensemble method that deals with long-memory time series. Our method starts by segmenting historical time series data to identify data segments which present model consistency. Then, we project the time series by using data segments which are close to current data. By using a dynamic time warping alignment function, we try to anticipate concept shifts, looking for similarities between current data and the prequel of a past shift. We evaluate our proposal on non-stationary and non-linear time series. To achieve this we perform forecasting accuracy testing against well known state-of-the-art methods such as neural networks and threshold auto regressive models. Our results show that the proposed method anticipates many concept shifts.

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  • (2022)Telemetry Parameter Prediction of Spacecraft Power System Based on Concept DriftProceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021)10.1007/978-981-16-9492-9_182(1841-1852)Online publication date: 18-Mar-2022
  • (2018)Automatic Segmentation of Dynamic Network Sequences with Node LabelsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.277177630:3(407-420)Online publication date: 1-Mar-2018

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  1. Long-memory time series ensembles for concept shift detection

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    cover image ACM Conferences
    BigMine '13: Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
    August 2013
    119 pages
    ISBN:9781450323246
    DOI:10.1145/2501221
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 11 August 2013

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    1. long-term forecasting
    2. streaming

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    BigMine '13 Paper Acceptance Rate 13 of 23 submissions, 57%;
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    • (2022)Telemetry Parameter Prediction of Spacecraft Power System Based on Concept DriftProceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021)10.1007/978-981-16-9492-9_182(1841-1852)Online publication date: 18-Mar-2022
    • (2018)Automatic Segmentation of Dynamic Network Sequences with Node LabelsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.277177630:3(407-420)Online publication date: 1-Mar-2018

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