Mesochronal Structure Learning
By Sergey M. Plis, David Danks, Jianyu Yang. 2015
11 citations recorded by the source corpus.
Standard time series structure learning algorithms assume that the measurement timescale is approximately the same as the timescale of the underlying (causal) system. In many scientific contexts, however, this assumption is violated: the measurement timescale can be substantially slower than the system timescale (so intermediate time series datapoints will be missing). This assumption violation can lead to significant learning errors. In this paper, we provide a novel learning algorithm to extract system-timescale structure from measurement data that undersample the underlying system. We employ multiple algorithmic optimizations that exploit the problem structure in order to achieve computational tractability. The resulting algorithm is highly reliable at extracting system-timescale structure from undersampled data.
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- Kun Zhang
- Mingming Gong
- Bernhard Schölkopf
- Clark Glymour
- Dacheng Tao
- Biwei Huang
- Jan Peters
- Dominik Janzing
Citations in the corpus
- Causal Discovery from Temporally Aggregated Time Series (2017, methodology): “Recently, a set of methods have been proposed to estimate the causal structure at the causal frequency from subsampled data without resorting to disaggregation techniques (Hyttinen et al., 2016; Gong et al., 2015; Plis et al., 2015a; Danks & Plis, 2013).”
- Rate-Agnostic (Causal) Structure Learning (2015, methodology): “The problem of causal learning from “under-sampled” time series data was explicitly addressed by [6, 7], but they assumed that the degree of undersampling—i.e., the ratio of τ S to τ M —was both known and small.”