TSUBASA: Climate Network Construction on Historical and Real-Time Data

A climate network represents the global climate system by the interactions of a set of anomaly time-series. Network science has been applied to climate data to study the dynamics of a climate network. The core task to enable network dynamics analysis on climate data is the efficient computation and update of the correlation matrix for user-defined time-windows on historical and real-time data. We present TSUBASA, an algorithm for efficiently computing the exact pair-wise time-series correlation based on Pearson's correlation. By pre-computing simple and low-overhead sketches, TSUBASA can efficiently compute exact pairwise correlations on arbitrary time windows at query time. For real-time data, TSUBASA proposes a fast and incremental way of updating the correlation matrix. We provide a detailed time and space complexity analysis of TSUBASA. Our experiments show that with the same space overhead as a DFT-based approximate solution, TSUBASA has a lower sketching time and is on par with the approximate solution with respect to query time. TSUBASA is at least one order of magnitude faster than a baseline for both historical and real-time data.