Effect of Data Representation Method for Effective Mining of Time Series Data

In recent days, generation, collection and storage of large volume of data is increasing due to enhanced sensor and memory technology. Analysis and mining of these big data in various real life problems from health care to disaster prevention is also becoming possible with the ever increasing computational power and maginificant growth of powerful machine learning algorithms. The dynamics of natural or man made disasters can be captured by time series data and their efficient analysis can lead to the development of effective systems to minimize the loss or damage. Efficient techniques and algorithms for time series analysis is a challenging problem. Traditional machine learning algorithms for analysis of static data cannot be directly applied for analyzing dynamic time series data. For analysis of time series, representation of data and the comparison method are very important. Various methods for representation and similarity measurement of time series data have been proposed. In this paper, a comparative study has been done by simulation experiments with 85 bench mark data sets as a first step to study the effect of representation of data for mining task. The main focus of this paper is to study the effect of classification with raw data using different similarity measures with traditional classifiers and convolutional neural network and transformed data in to recurrence plot, classified by convolutional neural network. From the simulation results, it is found that classification accuracy with convolutional neural networks in many data sets are improved with time series represented by recurrence plot.

[1]  K. Tamilarasi,et al.  A survey on signature verification based algorithms , 2017 .

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[4]  Yixin Chen,et al.  Multi-Scale Convolutional Neural Networks for Time Series Classification , 2016, ArXiv.

[5]  Zhaohui Wu,et al.  Accelerometer-Based Gait Recognition by Sparse Representation of Signature Points With Clusters , 2015, IEEE Transactions on Cybernetics.

[6]  F. Viégas,et al.  Deep learning of aftershock patterns following large earthquakes , 2018, Nature.

[7]  Henry D. I. Abarbanel,et al.  Analysis of Observed Chaotic Data , 1995 .

[8]  B. Chakraborty,et al.  Efficient On-line Handwritten Signature Verification by Integrating DTW and Proposed Local Dynamic Similarity Measure , 2012 .

[9]  D. Ruelle,et al.  Recurrence Plots of Dynamical Systems , 1987 .

[10]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[11]  Vadim V. Strijov,et al.  Human activity recognition using quasiperiodic time series collected from a single tri-axial accelerometer , 2016, Multimedia Tools and Applications.

[12]  Tim Oates,et al.  Time series classification from scratch with deep neural networks: A strong baseline , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

[13]  Basabi Chakraborty,et al.  Temporal Awareness of Needs after East Japan Great Earthquake using Latent Semantic Analysis , 2013, EJC.

[14]  Basabi Chakraborty,et al.  Topic transition detection about the East Japan great earthquake based on emerging modularity over time , 2015, Int. J. Comput. Sci. Eng..

[15]  Mehmet Emre Çek,et al.  Analysis of observed chaotic data , 2004 .

[16]  M. Suteanu.  Scale free properties in a network-based integrated approach to earthquake pattern analysis , 2014 .