Extracting Sparse and Ununiform Lag Structure Inherent in VAR Models with Latent Group Regularized Learning

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Other Title
  • 潜在構造正則化学習を用いたベクトル自己回帰モデルに内在するスパースで不均一な時間遅れ構造の抽出

Description

<p>In multivariate time series data, it is important to capture time-delay structure where the necessary lag length to explain each variable is different. However, from the viewpoint of effectively using structurally regularized learning, when we apply conventional structurally regularized learning methods such as the Group LASSO, some problems, which include loss of necessary coefficients, occur. To overcome these problems, we propose a method based on the Latent Group LASSO regularization. To apply it to VAR models, we introduce overlapped coefficient groups in the time direction and use a heavy-tailed distribution, such as Student's $t$-distribution, as a prior to enhancing its sparsity. We evaluated the effectiveness of the proposed method using artificial data and actual data set (Beijing PM2.5 data set). As a result, we obtained the sparse models that capture the ununiform time-delay structure represented by fewer variables with the same degree of forecast accuracy comparing with ordinary VAR models.</p>

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Details 詳細情報について

  • CRID
    1390285300166153856
  • NII Article ID
    130007857009
  • DOI
    10.11517/pjsai.jsai2020.0_2p5gs302
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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