[2J1-GS-10] AI application:

[2J1-GS-10-05] Optimizing Outlier Detection in Behavior Prediction Models Using Correlation and Periodicity among Users

〇Akito Shitara¹ (1. Kyoto University)

Keywords:Outlier Detection, Time Series Analysis, Neural Network

In this study, we consider incorporating time-series forecasting into applications. We use a foundation model for time-series forecasting, and perform forecasting only by prior learning, without any fine tuning. In addition, this study assumes the existence of correlation and periodicity in the behavior of application users.
It is difficult to identify whether the outliers that occur in a time-series forecast are specific values that depend on the date (referred to as "events") or user-specific outliers.
We propose a method to identify event patterns and outliers without prior information on event dates and without modifying the model itself to maintain the generality of the foundation model.