Effective temporal dependence discovery in time series data

Abstract

To analyze user behavior over time, it is useful to group users into cohorts, giving rise to cohort analysis. We identify several crucial limitations of current cohort analysis, motivated by the unmet need for temporal dependence discovery. To address these limitations, we propose a generalization that we call recurrent cohort analysis. We introduce a set of operators for recurrent cohort analysis and design access methods specific to these operators in both single-node and distributed environments. Through extensive experiments, we show that recurrent cohort analysis when implemented using the proposed access methods is up to six orders faster than one implemented as a layer on top of a database in a single-node setting, and two orders faster than one implemented using Spark SQL in a distributed setting.

A Unified Approach for Multi-Scale Synchronous Correlation Search in Big Time Series -- Full Version

“…Sousa et al [16] use the intrinsic dimension to detect correlated attributes in databases. Cai et al in [8] use cohort analysis to look for causal explanations in the data. Yang et al [52] use a residue metric that measures the difference between the actual and expected value of an object to capture object correlations in large datasets.…”