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PLAE: Time-Series Prediction Improvement by Adaptive Decomposition

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PRICAI 2022: Trends in Artificial Intelligence (PRICAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13629))

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Abstract

Univariate time-series forecasting is a kind of commonly encountered yet tough problem. Most of the forecast algorithms’ performance is constrained by the limited information due to the single input dimension. No matter how capable a forecast algorithm is, an accurate output cannot be rendered on an unpredictable time-series. This paper presents PLAE (Predictability Leveraging Auto-Encoder), a Seq2Seq model for univariate time-series data aiming to enhance the accuracy of the given algorithm without dimensional adaptation. The main idea is decomposing the original input data into a group of more predictable microscopic time-series on which the forecast algorithm can deliver a more accurate output. And the final prediction is rendered by aggregating those components back to the original one-dimension. Experiments on three public data sets and one real-world data set show that PLAE can improve the forecast accuracy for 23.38% in terms of MAPE and 19.76% in terms of RMSE. Besides, experimental evidence shows that PLAE’s adaptive non-linear decomposition mechanism outperforms the pre-defined additive decomposition w.r.t. both forecasting performance and components’ interpretability.

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Notes

  1. 1.

    https://archive.ics.uci.edu/ml/datasets/PEMS-SF.

  2. 2.

    https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014.

  3. 3.

    https://robjhyndman.com/expsmooth/.

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Authors and Affiliations

  1. Lenovo Research, Beijing, China

    Jufang Duan, Yi Wang & Wei Zheng

Authors
  1. Jufang Duan
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  2. Yi Wang
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  3. Wei Zheng
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Correspondence to Jufang Duan .

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Editors and Affiliations

  1. CSIRO Australian e-Health Research Centre, Brisbane, QLD, Australia

    Sankalp Khanna

  2. Shanghai Jiao Tong University, Shanghai, China

    Jian Cao

  3. University of Tasmania, Hobart, TAS, Australia

    Quan Bai

  4. University of Technology Sydney, Sydney, NSW, Australia

    Guandong Xu

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Duan, J., Wang, Y., Zheng, W. (2022). PLAE: Time-Series Prediction Improvement by Adaptive Decomposition. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13629. Springer, Cham. https://doi.org/10.1007/978-3-031-20862-1_29

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  • DOI: https://doi.org/10.1007/978-3-031-20862-1_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20861-4

  • Online ISBN: 978-3-031-20862-1

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