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Temporal sequences have become pervasive in various real-world applications.Consequently, the volume of data generated in the form of continuous time-eventsequence(s) or CTES(s) has increased exponentially in the past few years. Thus,a significant fraction of the ongoing research on CTES datasets involvesdesigning models to address downstream tasks such as next-event prediction,long-term forecasting, sequence classification etc. The recent developments inpredictive modeling using marked temporal point processes (MTPP) have enabledan accurate characterization of several real-world applications involving theCTESs. However, due to the complex nature of these CTES datasets, the task oflarge-scale retrieval of temporal sequences has been overlooked by the pastliterature. In detail, by CTES retrieval we mean that for an input querysequence, a retrieval system must return a ranked list of relevant sequencesfrom a large corpus. To tackle this, we propose NeuroSeqRet, afirst-of-its-kind framework designed specifically for end-to-end CTESretrieval. Specifically, NeuroSeqRet introduces multiple enhancements overstandard retrieval frameworks and first applies a trainable unwarping functionon the query sequence which makes it comparable with corpus sequences,especially when a relevant query-corpus pair has individually differentattributes. Next, it feeds the unwarped query sequence and the corpus sequenceinto MTPP-guided neural relevance models. We develop four variants of therelevance model for different kinds of applications based on the trade-offbetween accuracy and efficiency. We also propose an optimization framework tolearn binary sequence embeddings from the relevance scores, suitable for thelocality-sensitive hashing. Our experiments show the significant accuracy boostof NeuroSeqRet as well as the efficacy of our hashing mechanism.

Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashing