A Time Series Classification Method for Battery Event Detection

The maintenance of batteries used in wireless mobile communication is an important practical problem. The experts can easily recognize the battery events, such as turning on, by watching the monitoring data. However it is infeasible to have experts watch the data all the time. There are devices that can report battery events. These devices sometimes report incorrect event. In order to solve this problem, we propose a time series classification framework to use the expert knowledge to build an accurate classifier, and then use the classifier to monitor the batteries in real time. We first propose an active learning method to efficiently collect the experts’ labels for each event. Then we apply various feature extraction methods to convert each time series segment into a feature vector. Finally, we apply random forest classifier to perform the classification. Moreover, in practice, the labeled data is unbalanced, i.e. >99% of the data instances belong to a single label. We use bootstrap to solve this problem. We test our method on a dataset for 500 batteries in 3 months. The results show that our method achieves a very high classification accuracy, using only less than 1% of the dataset as training set.

[1]  Antanas Verikas,et al.  Mining data with random forests: A survey and results of new tests , 2011, Pattern Recognit..

[2]  Christos Faloutsos,et al.  RainMon: an integrated approach to mining bursty timeseries monitoring data , 2012, KDD.

[3]  Geoffrey I. Webb,et al.  Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification , 2014, 2014 IEEE International Conference on Data Mining.

[4]  Li Wei,et al.  Fast time series classification using numerosity reduction , 2006, ICML.

[5]  Jason Lines,et al.  An Experimental Evaluation of Nearest Neighbour Time Series Classification , 2014, ArXiv.

[6]  Jingrui He,et al.  Nearest-Neighbor-Based Active Learning for Rare Category Detection , 2007, NIPS.

[7]  Andrew McCallum,et al.  Toward Optimal Active Learning through Monte Carlo Estimation of Error Reduction , 2001, ICML 2001.

[8]  Suman Nath,et al.  ThermoCast: a cyber-physical forecasting model for datacenters , 2011, KDD.

[9]  Huanhuan Chen,et al.  Model Metric Co-Learning for Time Series Classification , 2015, IJCAI.

[10]  Alexandros Nanopoulos,et al.  Time-Series Classification in Many Intrinsic Dimensions , 2010, SDM.

[11]  Dunja Mladenic,et al.  A probabilistic approach to nearest-neighbor classification: naive hubness bayesian kNN , 2011, CIKM '11.

[12]  Rong Jin,et al.  Batch mode active learning and its application to medical image classification , 2006, ICML.

[13]  Hossein Hamooni,et al.  Dual-Domain Hierarchical Classification of Phonetic Time Series , 2014, 2014 IEEE International Conference on Data Mining.

[14]  Eamonn J. Keogh,et al.  Towards a minimum description length based stopping criterion for semi-supervised time series classification , 2013, 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI).

[15]  Jason Lines,et al.  A shapelet transform for time series classification , 2012, KDD.

[16]  Li Wei,et al.  Semi-supervised time series classification , 2006, KDD '06.

[17]  Arnold W. M. Smeulders,et al.  Active learning using pre-clustering , 2004, ICML.

[18]  Eamonn J. Keogh,et al.  Experimental comparison of representation methods and distance measures for time series data , 2010, Data Mining and Knowledge Discovery.

[19]  Lars Schmidt-Thieme,et al.  INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification , 2011, PAKDD.

[20]  Eamonn J. Keogh,et al.  Logical-shapelets: an expressive primitive for time series classification , 2011, KDD.

[21]  Mark Craven,et al.  Multiple-Instance Active Learning , 2007, NIPS.

[22]  Eamonn J. Keogh,et al.  DTW-D: time series semi-supervised learning from a single example , 2013, KDD.

[23]  M. L. Fisher,et al.  An analysis of approximations for maximizing submodular set functions—I , 1978, Math. Program..

[24]  Qiong Luo,et al.  ACTS: An Active Learning Method for Time Series Classification , 2017, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).