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Dr. Xiaoqian Wang

I am a researcher specialising in big data time series forecasting. Currently, I serve as an Associate Editor for The R Journal and am an Associate Investigator at the Australian Research Council’s OPTIMA Centre.

From January 2023 to January 2025, I was a Postdoctoral Research Fellow in the Department of Econometrics & Business Statistics at Monash University, supervised by Prof. Rob J Hyndman. I earned my Ph.D. in Statistics from Beihang University, where I was supervised by Prof. Yanfei Kang. My doctoral thesis focused on Time Series Forecast Combinations from Multiple Perspectives.

Interests

  • Time Series Forecasting
  • Constrained Optimization
  • Distributed Computing
  • Statistical Modeling

Education

  • Ph.D. in Statistics, Sep 2018-Dec 2022

    Beihang University (Supervised by Yanfei Kang)

  • Visiting Ph.D. Student, Mar 2021‐Mar 2022

    Monash University (Supervised by Rob J Hyndman)

  • M. in Applied Statistics, Sep 2016-Jun 2018

    Central University of Finance and Economics

  • B.Ec in Statistics, Sep 2012-Jun 2016

    Shanxi University of Finance and Economics

Experience

 
 
 
 
 

Postdoctoral Research Fellow

Monash University

Jan 2023 – Jan 2025 Melbourne, Australia

Publications

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Optimal forecast reconciliation with time series selection

Forecast reconciliation methods that incorporate time series selection based on out-of-sample and in-sample information
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Online conformal inference for multi-step time series forecasting

Conformal prediction methods for multi-step time series forecasting
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Sparse Multiple Index Models for High-dimensional Nonparametric Forecasting

A novel algorithm for estimating a nonparametric/semi-parametric additive index model with optimal predictor selection and predictor …
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Forecast combinations: an over 50-year review

An up-to-date review of the literature on forecast combinations
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Distributed ARIMA models for ultra-long time series

Distributed ARIMA models implemented with Apache Spark
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Recent & Upcoming Talks

Optimal forecast reconciliation with time series selection

Talk at ISF 2024.
Jul 3, 2024 12:40 PM — 1:00 PM Dijon, France
Slides Follow

Optimal forecast reconciliation with time series selection

Talk at OPTIMA Seminar.
Mar 13, 2024 4:00 PM — 5:00 PM Monash University, VIC, Australia
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Another look at forecast trimming for combinations: robustness, accuracy and diversity

Talk at IASC-ARS 2023.
Dec 6, 2023 1:30 PM — 2:00 PM Macquarie University, NSW, Australia
Slides Follow

Forecast reconciliation with subset selection

Talk at 2023 IIF Workshop on Forecast Reconciliation.
Sep 7, 2023 2:15 PM — 3:00 PM Monash University Prato Centre, Italy
Slides Follow

Another look at forecast trimming for combinations: robustness, accuracy and diversity

Talk at ISF 2023.
Jun 28, 2023 10:00 AM — 10:20 AM Charlottesville, USA
PDF Slides Follow
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Recent Posts

Invert SARIMA models to AR

SARIMA Seasonal Autoregressive Integrated Moving Average, SARIMA or Seasonal ARIMA, is an extension of ARIMA that explicitly supports …
Nov 7, 2019 10 min read

Tutor

 
 
 
 
 

ETC3550/5550: Applied Forecasting

Monash University

Feb 2024 – May 2024 Melbourne, Australia
Reliable forecasts of business and economic variables must often be obtained against a backdrop of structural change in markets and the economy. This unit introduces methods suitable for forecasting in these circumstances including the decomposition of time series, exponential smoothing methods, ARIMA modelling, and regression with auto-correlated disturbances. Students can expect to enhance their computer skills with exercises using R.
 
 
 
 
 

ETC3550/5550: Applied Forecasting

Monash University

Feb 2023 – May 2023 Melbourne, Australia
The unit has been recognised by students as being among the top performing units in the Student Evaluation of Teaching and Units (SETU) conducted during the Semester 1, 2023 evaluation period.
 
 
 
 
 

Business Forecasting

Beihang University

Jul 2022 – Jul 2022 Beijing, China
For MBA and MPAcc students, 2020 Autumn and 2022 Autumn.
 
 
 
 
 

Big Data Essentials

Beihang University

Sep 2021 – Jan 2022 Beijing, China
Undergraduate level.
 
 
 
 
 

Applied Statistics

Beihang University

Feb 2020 – May 2022 Beijing, China
Undergraduate level, 2020-2022 Spring.

Featured Awards

IIF-SAS Grant (Methodology)

International Institute of Forecasters Dec 2024
For the project proposal in the category of Methodology, Valid and Efficient Conformal Inference for Multi-step Time Series Forecasting. The grant is provided by IIF, in collaboration and with financial support from SAS®, to promote research on Forecasting.

Graduate Student Award for Outstanding Academic and Innovative Achievements

Beihang University Apr 2024
Honored by Beihang University in April 2024

Outstanding Graduate Student

Beihang University Dec 2022
Outstanding graduate student (First Prize) honored by Beihang University in December 2022

Best Paper Award

Graduate School, CUEB (Capital University of Economics and Business), China Nov 2022
The paper, Distributed ARIMA models for ultra-long time series, was awarded as the best paper in the Statistics and Quantitative Economics Sub-Forum of the 13th HUB-University (Economics and Management) Doctoral Academic Forum held by CUEB (17-18 November 2022, VIRTUAL)

Best Paper Award

POMS (Production and Operations Management Society) Chinese Summer School Sep 2022
The paper, Distributed ARIMA models for ultra-long time series, was awarded as the best paper of POMS Chinese Summer School 2022 (26-30 September 2022, VIRTUAL)

Best Student Presentation Award

International Institute of Forecasters Oct 2020
In recognition of an inspiring, effective and professional student speaker at the 40th International Symposium on Forecasting (26-28 October 2020, VIRTUAL)

CSC (China Scholarship Council) Chinese Government Scholarship for Studying Abroad

China Scholarship Council Aug 2020
Have been awarded the Chinese Government Scholarship to sponsor my study aboard

Popular Topics

ARIMA models Combination selection Conformal prediction Coverage guarantee Dimension reduction Distributed forecasting Distribution-free inference Diversity Forecast combinations Forecast ensembles Forecast pooling Forecast reconciliation Forecast trimming Forecasting Integer programming Optimization Review Spark Time series features Uncertainty estimation

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