41 research outputs found
Causal Discovery from Temporal Data: An Overview and New Perspectives
Temporal data, representing chronological observations of complex systems,
has always been a typical data structure that can be widely generated by many
domains, such as industry, medicine and finance. Analyzing this type of data is
extremely valuable for various applications. Thus, different temporal data
analysis tasks, eg, classification, clustering and prediction, have been
proposed in the past decades. Among them, causal discovery, learning the causal
relations from temporal data, is considered an interesting yet critical task
and has attracted much research attention. Existing casual discovery works can
be divided into two highly correlated categories according to whether the
temporal data is calibrated, ie, multivariate time series casual discovery, and
event sequence casual discovery. However, most previous surveys are only
focused on the time series casual discovery and ignore the second category. In
this paper, we specify the correlation between the two categories and provide a
systematical overview of existing solutions. Furthermore, we provide public
datasets, evaluation metrics and new perspectives for temporal data casual
discovery.Comment: 52 pages, 6 figure
Forecasting of commercial sales with large scale Gaussian Processes
This paper argues that there has not been enough discussion in the field of
applications of Gaussian Process for the fast moving consumer goods industry.
Yet, this technique can be important as it e.g., can provide automatic feature
relevance determination and the posterior mean can unlock insights on the data.
Significant challenges are the large size and high dimensionality of commercial
data at a point of sale. The study reviews approaches in the Gaussian Processes
modeling for large data sets, evaluates their performance on commercial sales
and shows value of this type of models as a decision-making tool for
management.Comment: 1o pages, 5 figure
Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
Discovering causal relationship using multivariate functional data has
received a significant amount of attention very recently. In this article, we
introduce a functional linear structural equation model for causal structure
learning when the underlying graph involving the multivariate functions may
have cycles. To enhance interpretability, our model involves a low-dimensional
causal embedded space such that all the relevant causal information in the
multivariate functional data is preserved in this lower-dimensional subspace.
We prove that the proposed model is causally identifiable under standard
assumptions that are often made in the causal discovery literature. To carry
out inference of our model, we develop a fully Bayesian framework with suitable
prior specifications and uncertainty quantification through posterior
summaries. We illustrate the superior performance of our method over existing
methods in terms of causal graph estimation through extensive simulation
studies. We also demonstrate the proposed method using a brain EEG dataset.Comment: 36 pages, 2 figures, 7 table