Summary:

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. In this interview Draco talks about TSUBASA, an algorithm for the efficient construction of climate networks based on the exact calculation of Pearson’s 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. 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.

Questions:

0:54 - Can you introduce your work, describe the problem your paper is aiming to solve and the motivation for doing so?

4:11 - What is the solution you developed? How did you tackle the problem?

6:50 - What is the improvement of TSUBASA over existing work?

8.59 - Are your tools/algorithms publicly available?

10:21 - What is the most interesting lesson or challenge faced whilst working on this topic?

11:51 - What are the future directions for your research?

15:43 - Are there other domains your research can be applied to?

Links:

• Homepage
• Paper (arXiv)
• tsupy library

Contact Info:

• Email: [email protected]
• Twitter: @DracoyunlongXu

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