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

TL;DR

TSUBASA is an efficient algorithm for constructing and updating climate networks using exact Pearson correlation calculations, enabling real-time interactive analysis of large climate datasets.

Contribution

It introduces a novel pre-computation approach for fast, exact correlation calculation and incremental updates for real-time climate network analysis.

Findings

TSUBASA is at least 10 times faster than approximate methods.

It outperforms baseline correlation calculation methods by up to 100 times.

The algorithm enables real-time updates of climate networks.

Abstract

A climate network represents the global climate system by the interactions of a set of anomaly time-series. Network science has been applied on climate data to study the dynamics of a climate network. The core task and first step to enable interactive network science on climate data is the efficient construction and update of a climate network on user-defined time-windows. We present TSUBASA, an algorithm for the efficient construction of climate networks based on the exact calculation of Pearsons correlation of large time-series. By pre-computing simple and low-overhead statistics, TSUBASA can efficiently compute the exact pairwise correlation of time-series on arbitrary time windows at query time. For real-time data, TSUBASA proposes a fast and incremental way of updating a network at interactive speed. Our experiments show that TSUBASA is faster than approximate solutions at least one order of magnitude for both historical and real-time data and outperforms a baseline for time-series correlation calculation up to two orders of magnitude.