Forecasting Intermittent Demand Patterns with Time Series and Machine Learning Methodologies

Yuwen Hong

Purdue University

Yuwen Hong is a graduate student in Business Analytics and Information Management program of Purdue University. During her graduate studies, she participated in several business analytical projects, focusing on predictive analytics and machine learning. Through these projects, she became familiar with the whole business analytics process, from data collection to application, and thus prepared herself for creating unique solutions for companies or organizations that want to compete in the Big Data Era with analytics.

Jingda Zhou

Purdue University

Jingda is a current graduate student in MS Business Analytics and Info Mgmt program at Purdue University. Prior to Purdue, she conducted research on how hurtful events influence the way people communicate with their partners, and built regression models to identify behavioral patterns. During her internship as a marketing analyst at MullenLowe, she conducted consumer analytics, web analytics and digital campaign optimization.


The study aims to generalize the predictive accuracy of various machine learning approaches, along with the widely used Croston’s method for time-series forecasting. Using multiple multi-period time-series we see if there is... [ view full abstract ]


  1. Yuwen Hong (Purdue University)
  2. Jingda Zhou (Purdue University)
  3. Matthew Lanham (Purdue University)

Topic Area

Topics: Topic #1


SPP-2 » Student Presentations & Posters (10:45 - Friday, 13th April, Haymarket Station B)



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