Compact Dynamic Inner Canonical Correlation Analysis for Nonstationary Dynamic Feature Extraction and Prediction

Junhao CHEN; S. Joe QIN

doi:10.1016/j.ifacol.2023.10.1455

Compact Dynamic Inner Canonical Correlation Analysis for Nonstationary Dynamic Feature Extraction and Prediction

Junhao CHEN, S. Joe QIN^*

^*Corresponding author for this work

LEO Dr David P. Chan Institute of Data Science

Research output: Journal Publications › Journal Article (refereed) › peer-review

Abstract

In this paper, a novel compact dynamic inner canonical correlation analysis (DiCCA) algorithm with autoregressive integrated moving average (ARIMA) inner models is proposed to better capture the latent dynamics of high dimensional time series. It can extract latent factors that capture the underlying dynamics of the data and model them using the ARIMA structure, which has fewer parameters and more flexibility than the potentially high-order AR structure used in the original DiCCA algorithm. The proposed algorithm can also handle nonstationary latent factors by explicitly modeling the unit roots. The algorithm integrates the extraction and the modeling of a latent factor in one step, resulting in a consistent inner and outer model. The algorithm is applied to an industrial dataset and shows better performance than the original DiCCA algorithm.

Original language	English
Pages (from-to)	3190-3196
Number of pages	7
Journal	IFAC-PapersOnLine
Volume	56
Issue number	2
Early online date	22 Nov 2023
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.1455
Publication status	Published - 2023
Event	22nd IFAC World Congress - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023

Bibliographical note

Publisher Copyright:
Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Funding

Partial financial support for this work from a General Research Fund by the Research Grants Council (RGC) of Hong Kong SAR, China (Project No. 11303421), a Collaborative Research Fund by RGC of Hong Kong (Project No. C1143-20G), a grant from the Natural Science Foundation of China (U20A20189), a grant from ITF-Guangdong-Hong Kong Technology Cooperation Funding Scheme (Project Ref. No. GHP/145/20), a Math and Application Project (2021YFA1003504) under the National Key R&D Program, a Shenzhen-Hong Kong-Macau Science and Technology Project Category C (9240086), and an InnoHK initiative of The Government of the HKSAR for the Laboratory for AI-Powered Financial Technologies is gratefully acknowledged.

Keywords

ARIMA model
canonical correlation
Dynamic latent variable model
nonstationary process
reduced-dimensional dynamics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.ifacol.2023.10.1455Licence: CC BY-NC-ND

Cite this

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title = "Compact Dynamic Inner Canonical Correlation Analysis for Nonstationary Dynamic Feature Extraction and Prediction",

abstract = "In this paper, a novel compact dynamic inner canonical correlation analysis (DiCCA) algorithm with autoregressive integrated moving average (ARIMA) inner models is proposed to better capture the latent dynamics of high dimensional time series. It can extract latent factors that capture the underlying dynamics of the data and model them using the ARIMA structure, which has fewer parameters and more flexibility than the potentially high-order AR structure used in the original DiCCA algorithm. The proposed algorithm can also handle nonstationary latent factors by explicitly modeling the unit roots. The algorithm integrates the extraction and the modeling of a latent factor in one step, resulting in a consistent inner and outer model. The algorithm is applied to an industrial dataset and shows better performance than the original DiCCA algorithm.",

keywords = "ARIMA model, canonical correlation, Dynamic latent variable model, nonstationary process, reduced-dimensional dynamics",

author = "Junhao CHEN and QIN, {S. Joe}",

note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/); 22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023",

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AU - QIN, S. Joe

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N2 - In this paper, a novel compact dynamic inner canonical correlation analysis (DiCCA) algorithm with autoregressive integrated moving average (ARIMA) inner models is proposed to better capture the latent dynamics of high dimensional time series. It can extract latent factors that capture the underlying dynamics of the data and model them using the ARIMA structure, which has fewer parameters and more flexibility than the potentially high-order AR structure used in the original DiCCA algorithm. The proposed algorithm can also handle nonstationary latent factors by explicitly modeling the unit roots. The algorithm integrates the extraction and the modeling of a latent factor in one step, resulting in a consistent inner and outer model. The algorithm is applied to an industrial dataset and shows better performance than the original DiCCA algorithm.

AB - In this paper, a novel compact dynamic inner canonical correlation analysis (DiCCA) algorithm with autoregressive integrated moving average (ARIMA) inner models is proposed to better capture the latent dynamics of high dimensional time series. It can extract latent factors that capture the underlying dynamics of the data and model them using the ARIMA structure, which has fewer parameters and more flexibility than the potentially high-order AR structure used in the original DiCCA algorithm. The proposed algorithm can also handle nonstationary latent factors by explicitly modeling the unit roots. The algorithm integrates the extraction and the modeling of a latent factor in one step, resulting in a consistent inner and outer model. The algorithm is applied to an industrial dataset and shows better performance than the original DiCCA algorithm.

KW - ARIMA model

KW - canonical correlation

KW - Dynamic latent variable model

KW - nonstationary process

KW - reduced-dimensional dynamics

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DO - 10.1016/j.ifacol.2023.10.1455

M3 - Journal Article (refereed)

AN - SCOPUS:85184963507

SN - 2405-8963

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Compact Dynamic Inner Canonical Correlation Analysis for Nonstationary Dynamic Feature Extraction and Prediction

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

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