Mining Sub-Interval Relationships In Time Series Data
This addresses the need for more precise pattern discovery in time-series data across domains like neuroscience and climate science, though it is incremental as it builds on traditional whole-series methods.
The paper tackles the problem of discovering relationships between time series that occur only in specific sub-intervals, proposing an efficient method to identify these sub-interval relationships (SIRs). Results show statistically significant SIRs in climate science and neuroscience datasets, with some having physical interpretations.
Time-series data is being increasingly collected and stud- ied in several areas such as neuroscience, climate science, transportation, and social media. Discovery of complex patterns of relationships between individual time-series, using data-driven approaches can improve our understanding of real-world systems. While traditional approaches typically study relationships between two entire time series, many interesting relationships in real-world applications exist in small sub-intervals of time while remaining absent or feeble during other sub-intervals. In this paper, we define the notion of a sub-interval relationship (SIR) to capture inter- actions between two time series that are prominent only in certain sub-intervals of time. We propose a novel and efficient approach to find most interesting SIR in a pair of time series. We evaluate our proposed approach on two real-world datasets from climate science and neuroscience domain and demonstrated the scalability and computational efficiency of our proposed approach. We further evaluated our discovered SIRs based on a randomization based procedure. Our results indicated the existence of several such relationships that are statistically significant, some of which were also found to have physical interpretation.