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Explainable Bayesian Models for Smooth Time Series Datasets (부드러운 시계열 함수를 설명하는 베이지안 추론 SW)
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Relational Automatic Statistician

Note that, this software is based on the automatic statistician system,

This repository provides the source codes for the paper.

Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series by Yunseong Hwang, Anh Tong, Jaesik Choi in ICML-2016


Gaussian Processes (GPs) provide a general and analytically tractable way of modeling complex time-varying, nonparametric functions. The Automatic Bayesian Covariance Discovery (ABCD) system constructs natural-language description of time-series data by treating unknown time-series data nonparametrically using GP with a composite covariance kernel function. Unfortunately, learning a composite covariance kernel with a single time-series data set often results in less informative kernel that may not give qualitative, distinctive descriptions of data. We address this challenge by proposing two relational kernel learning methods which can model multiple time-series data sets by finding common, shared causes of changes. We show that the relational kernel learning methods find more accurate models for regression problems on several real-world data sets; US stock data, US house price index data and currency exchange rate data.

This version of software is developed by Yunseong Hwang, Anh Tong and Jaesik Choi, members of Statistical Artificial Intelligence Laboratory (SAIL) at Ulsan National Institute of Science and Technology (UNIST), Korea.

If you have any question, Feel free to email the authors with any questions:

Yunseong Hwang (
Anh Tong (
Jaesik Choi (


Apache License 2.0


  • James Robert Lloyd, David Duvenaud, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani, Automatic Construction and Natural-Language Description of Nonparametric Regression Models, Association for the Advancement of Artificial Intelligence (AAAI) Conference, 2014.

XAI Project

Project Name

A machine learning and statistical inference framework for explainable artificial intelligence(의사결정 이유를 설명할 수 있는 인간 수준의 학습·추론 프레임워크 개발)

Managed by

Ministry of Science and ICT/XAIC

Participated Affiliation

UNIST, Korea Univ., Yonsei Univ., KAIST., AItrics

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