Power Series Classification: A Hybrid of LSTM and a Novel Advancing Dynamic Time Warping

As many applications organize data into temporal sequences, the problem of time series data classification has been widely studied. Recent studies show that the 1-nearest neighbor with dynamic time warping (1NN-DTW) and the long short term memory (LSTM) neural network can achieve a better performance than other machine learning algorithms. In this paper, we build a novel time series classification algorithm hybridizing 1NN-DTW and LSTM, and apply it to a practical data center power monitoring problem. Firstly, we define a new distance measurement for the 1NN-DTW classifier, termed as Advancing Dynamic Time Warping (ADTW), which is non-commutative and non-dynamic programming. Secondly, we hybridize the 1NN-ADTW and LSTM together. In particular, a series of auxiliary test samples generated by the linear combination of the original test sample and its nearest neighbor with ADTW are utilized to detect which classifier to trust in the hybrid algorithm. Finally, using the power consumption data from a real data center, we show that the proposed ADTW can improve the classification accuracy from about 84\% to 89\%. Furthermore, with the hybrid algorithm, the accuracy can be further improved and we achieve an accuracy up to about 92\%. Our research can inspire more studies on non-commutative distance measurement and the hybrid of the deep learning models with other traditional models.