Deviation-Based Marked Temporal Point Process for Marker Prediction

Abstract

Temporal Point Processes (TPPs) are useful for modeling event sequences which do not occur at regular time intervals. For example, TPPs can be used to model the occurrence of earthquakes, social media activity, financial transactions, etc. Owing to their flexible nature and applicability in several real-world scenarios, TPPs have gained wide attention from the research community. In literature, TPPs have mostly been used to predict the occurrence of the next event (time) with limited focus on the type/category of the event, termed as the marker. Further, limited focus has been given to model the inter-dependency of the event time and marker information for more accurate predictions. To this effect, this research proposes a novel Deviation-based Marked Temporal Point Process (DMTPP) algorithm focused on predicting the marker corresponding to the next event. Specifically, the deviation between the estimated and actual occurrence of the event is modeled for predicting the event marker. The DMTPP model is explicitly useful in scenarios where the marker information is not known immediately with the event occurrence, but is instead obtained after some time. DMTPP utilizes a Recurrent Neural Network (RNN) as its backbone for encoding the historical sequence pattern, and models the dependence between the marker and event time prediction. Experiments have been performed on three publicly available datasets for different tasks, where the proposed DMTPP model demonstrates state-of-the-art performance. For example, an accuracy of 91.76% is obtained on the MIMIC-II dataset, demonstrating an improvement of over 6% from the state-of-the-art model.