Abstract: 

In the fast-moving world today, rare events are becoming increasingly common. Ranging from studying incidents of safety hazards to identifying transaction fraud, they all fall under the radar of rare events. Identifying and studying rare events become of crucial importance, particularly when the underlying event conforms to a sensitive or an adverse issue. The thing to note here is, despite the probability of occurrence being very close to zero, the potential specification of the rare event could be quite extensive. For example, within the parent rare event of Product Safety, there could be multiple types of potential hazard (Fire, Electrical, Pharmaceutical, etc.), rendering the sub-classes rarer still. In this talk, we are going to discuss a novel algorithm designed to study a rare event and its sub-classes over time with a primary focus on forecast and detecting anomalies.

The anomalies studied here are relative anomalies i.e., they may not contribute to the long-term trend of the rare time series but represent deviation from the base state as seen in the immediate past.

Bio: 

Debanjana is a Data Scientist at Walmart Labs, Global Data Value Realization. At Walmart, she has been instrumental in building numerous high-functioning bots in the compliance space dealing heavily in Natural Language Processing, Optimization, Mixture Models, and Rare Time Series. Currently, her focus is on extensive Shrink Research where along with her team members she is identifying potential areas of high impact for Retail Shrink. During her 3 years of experience, Debanjana has filed 5 US patents in the field of Clustering & Anomaly Detection, Imbalance Text Classification, Travel Optimization, and Stochastic Processes. In addition, she has three published papers to her credit. She presented her paper REDCLAN (Relative Density-Based Clustering and Anomaly Detection) in ADCOM’18, CRESST was included at ICMLA'19 and iCASSTLE (Imbalanced Classification Algorithm for Semi-Supervised Text Learning) was presented at ICMLA'18 (Orlando, FL), which was later published by IEEE. Debanjana has a master's degree in Statistics from the Indian Institute of Technology (Kanpur).