About

Xiaojia Guo is an Assistant Professor of Decision and Operations at the Robert H. Smith School of Business. Her research interests lie in predictive analytics and decision analysis, with a specific focus on probabilistic forecasting and combining predictions from experts or models. She employs Bayesian statistics and machine learning techniques as primary methods in her research projects. Her work includes developing models for demand forecasting, particularly in the context of product life cycles, where she introduces a unified, robust, and interpretable approach inspired by Bayesian model averaging. Her models are designed to produce accurate pre- and post-launch distributional forecasts, capable of adapting to recent changes in a product’s life cycle through exponential smoothing and Bayesian updating. Her research aims to improve operational decision-making related to capacity, inventory, and marketing expenditures, and her models have demonstrated superior performance in empirical studies. Xiaojia Guo has been recognized through several prestigious awards and competitions, highlighting her contributions to operations research and decision analysis.

Research topics

• Computer Science
• Artificial Intelligence
• Operations research
• Engineering
• Data science
• Software engineering
• Machine Learning
• Political Science
• Management science

Selected publications

• Regularized Ensemble Forecasting for Learning Weights from Historical and Current Forecasts

  ArXiv.org · 2026-02-11

  articleOpen access

  Combining forecasts from multiple experts often yields more accurate results than relying on a single expert. In this paper, we introduce a novel regularized ensemble method that extends the traditional linear opinion pool by leveraging both current forecasts and historical performances to set the weights. Unlike existing approaches that rely only on either the current forecasts or past accuracy, our method accounts for both sources simultaneously. It learns weights by minimizing the variance of the combined forecast (or its transformed version) while incorporating a regularization term informed by historical performances. We also show that this approach has a Bayesian interpretation. Different distributional assumptions within this Bayesian framework yield different functional forms for the variance component and the regularization term, adapting the method to various scenarios. In empirical studies on Walmart sales and macroeconomic forecasting, our ensemble outperforms leading benchmark models both when experts' full forecasting histories are available and when experts enter and exit over time, resulting in incomplete historical records. Throughout, we provide illustrative examples that show how the optimal weights are determined and, based on the empirical results, we discuss where the framework's strengths lie and when experts' past versus current forecasts are more informative.

  PublisherOA PDF

• Regularized Ensemble Forecasting for Learning Weights from Historical and Current Forecasts

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Regularized Ensemble Forecasting for Learning Weights from Historical and Current Forecasts

  Open MIND · 2026-02-11

  preprint

  Combining forecasts from multiple experts often yields more accurate results than relying on a single expert. In this paper, we introduce a novel regularized ensemble method that extends the traditional linear opinion pool by leveraging both current forecasts and historical performances to set the weights. Unlike existing approaches that rely only on either the current forecasts or past accuracy, our method accounts for both sources simultaneously. It learns weights by minimizing the variance of the combined forecast (or its transformed version) while incorporating a regularization term informed by historical performances. We also show that this approach has a Bayesian interpretation. Different distributional assumptions within this Bayesian framework yield different functional forms for the variance component and the regularization term, adapting the method to various scenarios. In empirical studies on Walmart sales and macroeconomic forecasting, our ensemble outperforms leading benchmark models both when experts' full forecasting histories are available and when experts enter and exit over time, resulting in incomplete historical records. Throughout, we provide illustrative examples that show how the optimal weights are determined and, based on the empirical results, we discuss where the framework's strengths lie and when experts' past versus current forecasts are more informative.

  DOI

• Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts

  Manufacturing & Service Operations Management · 2024-10-30 · 5 citations

  article1st authorCorresponding

  Problem definition: We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory, and marketing expenditures. We introduce a unified, robust, and interpretable approach to producing these pre- and postlaunch distributional forecasts. Methodology/results: Our approach is inspired by Bayesian model averaging. Each candidate model in our ensemble is a life-cycle model fitted to the completed life cycle of a comparable product. A prelaunch forecast is an ensemble with equal weights on the candidate models’ forecasts, whereas a postlaunch forecast is an ensemble with weights that evolve according to Bayesian updating. Our approach is part frequentist and part Bayesian, resulting in a novel approach tailored to the demand forecasting challenge. We also introduce a new type of life-cycle or product diffusion model with states that can be updated using exponential smoothing. The trend in this model follows the density of an exponentially tilted Gompertz random variable. For postlaunch forecasting, this model is attractive because it can adapt itself to the most recent changes in a product’s life cycle. We provide closed-form distributional forecasts from our model. In two empirical studies, we show that when the ensemble’s candidate models are all in our new type of exponential smoothing model, this version of the ensemble outperforms several leading approaches in both point and quantile forecasting. Managerial implications: In a data-driven operations environment, our model can produce accurate forecasts frequently and at scale. When quantile forecasts are needed, our model has the potential to provide meaningful economic benefits. In addition, our model’s interpretability should be attractive to managers who already use exponential smoothing and ensemble methods for other forecasting purposes. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0359 .

  PublisherDOI

• Forecasting: theory and practice

  International Journal of Forecasting · 2022 · 807 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2021 The Author(s)

  PublisherOA PDFDOI

• Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts

  SSRN Electronic Journal · 2022-01-01 · 3 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Forecasting: theory and practice

  arXiv (Cornell University) · 2022 · 27 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

  Publisher

• Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning

  Manufacturing & Service Operations Management · 2021 · 53 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Operations research

  Problem definition: Airports and airlines have been challenged to improve decision making by producing accurate forecasts in real time. We develop a two-phased predictive system that produces forecasts of transfer passenger flows at an airport. In the first phase, the system predicts the distribution of individual transfer passengers’ connection times. In the second phase, the system samples from the distribution of individual connection times and produces distributional forecasts for the number of passengers arriving at the immigration and security areas. Academic/practical relevance: To our knowledge, this work is the first to apply machine learning for predicting real-time distributional forecasts of journeys in an airport using passenger level data. Better forecasts of these journeys can help optimize passenger experience and improve airport resource deployment. Methodology: The predictive system developed is based on a regression tree combined with copula-based simulations. We generalize the tree method to predict distributions, moving beyond point forecasts. We also formulate a newsvendor-based resourcing problem to evaluate decisions made by applying the new predictive system. Results: We show that, when compared with benchmarks, our two-phased approach is more accurate in predicting both connection times and passenger flows. Our approach also has the potential to reduce resourcing costs at the immigration and transfer security areas. Managerial implications: Our predictive system can produce accurate forecasts frequently and in real time. With these forecasts, an airport’s operating team can make data-driven decisions, identify late passengers, and assist them to make their connections. The airport can also update its resourcing plans based on the prediction of passenger flows. Our predictive system can be generalized to other operations management domains, such as hospitals or theme parks, in which customer flows need to be accurately predicted. Funding: This work was funded by Eurocontrol (APOC Business Process Reengineering Big Data Study) [Grant 15-220643-AWP6.3.1]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0975 .

  PublisherDOI

• London Heathrow Airport Uses Real-Time Analytics for Improving Operations

  SSRN Electronic Journal · 2020-01-01 · 3 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Probabilistic Forecasting in Decision-Making: New Methods and Applications

  UCL Discovery (University College London) · 2020-11-28 · 1 citations

  dissertation1st authorCorresponding

  This thesis develops new methods to generate probabilistic forecasts and applies these methods to solve operations problems in practice. The first chapter introduces a new product life cycle model, the tilted-Gompertz model, which can predict the distribution of period sales and cumulative sales over a product's life cycle. The tilted-Gompertz model is developed by exponential tilting the Gompertz model, which has been widely applied in modelling human mortality. Due to the tilting parameter, this new model is flexible and capable of describing a wider range of shapes compared to existing life cycle models. In two empirical studies, one on the adoption of new products and the other on search interest in social networking websites, I find that the tilted-Gompertz model performs well on quantile forecasting and point forecasting, when compared to other leading life-cycle models. In the second chapter, I develop a new exponential smoothing model that can capture life-cycle trends. This new exponential smoothing model can also be viewed as a tilted-Gompertz model with time-varying parameters. The model can adapt to local changes in the time series due to the smoothing parameters in the exponential smoothing formulation. When estimating the parameters, prior information is included in the regularization terms of the model. In the empirical studies, the new exponential smoothing model outperforms several leading benchmark models in predicting quantiles on a rolling basis. In the final chapter, I develop a predictive system that predicts distributions of passengers' connection times and transfer passenger flows at an airport using machine learning methods. The predictive system is based on regression trees and copula-based simulations. London Heathrow airport currently uses this proposed system and has reported significant accuracy improvements over their legacy systems.

  Publisher

Frequent coauthors

• Yael Grushka‐Cockayne

  University of Virginia

  12 shared

• John Lim

  11 shared

• Bert De Reyck

  4 shared

• Kenneth C. Lichtendahl

  Google (United States)

  4 shared

• Clara Cordeiro

  University of Algarve

  4 shared

• Michael Gilliland

  Forecasting International (United States)

  4 shared

• Michael P. Clements

  4 shared

• Florian Ziel

  University of Duisburg-Essen

  3 shared

Awards & honors

• M&SOM Practice-based Research Competition
• POMS-JD.com Best Data-Driven Research Paper Competition
• INFORMS Aviation Applications Best Paper Award
• INFORMS Decision Analysis Society’s Best Student Paper
• Daniel H. Wagner Prize for Excellence in Operations Research…