Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM

TL;DR

This paper introduces a novel Local Time Warping distance measure and a hybrid classification method combining 1NN-LTW and LSTM to improve power consumption series classification accuracy in data centers.

Contribution

It proposes a new non-commutative, non-dynamic programming distance measure called Local Time Warping and a hybrid classification approach integrating 1NN-LTW with LSTM.

Findings

LTW improves classification accuracy from 84% to 90%.

Hybrid method achieves up to 93% accuracy.

Linear version of LTW outperforms LB_Keogh.

Abstract

In this paper, for the purpose of data centre energy consumption monitoring and analysis, we propose to detect the running programs in a server by classifying the observed power consumption series. Time series classification problem has been extensively studied with various distance measurements developed; also recently the deep learning based sequence models have been proved to be promising. In this paper, we propose a novel distance measurement and build a time series classification algorithm hybridizing nearest neighbour and long short term memory (LSTM) neural network. More specifically, first we propose a new distance measurement termed as Local Time Warping (LTW), which utilizes a user-specified set for local warping, and is designed to be non-commutative and non-dynamic programming. Second we hybridize the 1NN-LTW and LSTM together. In particular, we combine the prediction probability vector of 1NN-LTW and LSTM to determine the label of the test cases. Finally, using the power consumption data from a real data center, we show that the proposed LTW can improve the classification accuracy of DTW from about 84% to 90%. Our experimental results prove that the proposed LTW is competitive on our data set compared with existed DTW variants and its non-commutative feature is indeed beneficial. We also test a linear version of LTW and it can significantly outperform existed linear runtime lower bound methods like LB_Keogh. Furthermore, with the hybrid algorithm, for the power series classification task we achieve an accuracy up to about 93%. Our research can inspire more studies on time series distance measurement and the hybrid of the deep learning models with other traditional models.