Time series reconstruction analysis

The dimensionality of time series data is usually very large, so it must often be reduced before applying certain data mining tasks upon it. Dimensionality reduction is achieved by creating appropriate time series representation that is actually new time series of lower dimensionality obtained from the original one by preserving only the important features. I addition to that, the reconstruction of original time series from its representation is inevitable task in many practical applications. The main objective of this paper is the comparison of different time series representations in the term of their reconstruction accuracies on a number of freely available data sets. Reconstructed time series are compared with the original ones, using several state-of-the-art similarity measures, in order to measure the quantity of information loss. Additionally, we measured the correlations between several data set properties and their reconstruction errors, which will give a deeper insight in the problem of choosing of appropriate representation technique for a particular data set.

[1]  Eamonn J. Keogh,et al.  Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases , 2001, Knowledge and Information Systems.

[2]  Yunhao Liu,et al.  Indexable PLA for Efficient Similarity Search , 2007, VLDB.

[3]  Abir Jaafar Hussain,et al.  Time Series Prediction Using Dynamic Ridge Polynomial Neural Networks , 2009, 2009 Second International Conference on Developments in eSystems Engineering.

[4]  Ponnuthurai N. Suganthan,et al.  Random vector functional link network for short-term electricity load demand forecasting , 2016, Inf. Sci..

[5]  Zoran Budimac,et al.  Case‐based curve behaviour prediction , 2009, Softw. Pract. Exp..

[6]  Lei Chen,et al.  Robust and fast similarity search for moving object trajectories , 2005, SIGMOD '05.

[7]  Giuseppe De Nicolao,et al.  A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Mirjana Ivanovic,et al.  The Influence of Global Constraints on Similarity Measures for Time-Series Databases , 2011, Knowl. Based Syst..

[9]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[10]  Ada Wai-Chee Fu,et al.  Efficient time series matching by wavelets , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[11]  Lipo Wang,et al.  Stock Forecasting with Feedforward Neural Networks and Gradual Data Sub-Sampling , 2010, Aust. J. Intell. Inf. Process. Syst..

[12]  Li Wei,et al.  Experiencing SAX: a novel symbolic representation of time series , 2007, Data Mining and Knowledge Discovery.

[13]  Eamonn Keogh.  Exact Indexing of Dynamic Time Warping , 2002, VLDB.

[14]  Mirjana Ivanovic,et al.  Comparison of different weighting schemes for the kNN classifier on time-series data , 2016, Knowledge and Information Systems.

[15]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[16]  Hui Ding,et al.  Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..

[17]  Eamonn J. Keogh,et al.  An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback , 1998, KDD.

[18]  Ming Zhu,et al.  Intelligent trading using support vector regression and multilayer perceptrons optimized with genetic algorithms , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[19]  Lei Chen,et al.  On The Marriage of Lp-norms and Edit Distance , 2004, VLDB.

[20]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[21]  Divyakant Agrawal,et al.  A comparison of DFT and DWT based similarity search in time-series databases , 2000, CIKM '00.

[22]  Ronen Feldman,et al.  The Data Mining and Knowledge Discovery Handbook , 2005 .

[23]  Zhiping Lin,et al.  Wavelet Packet Multi-layer Perceptron for Chaotic Time Series Prediction: Effects of Weight Initialization , 2001, International Conference on Computational Science.

[24]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[25]  Dong-Chul Park,et al.  A Time Series Data Prediction Scheme Using Bilinear Recurrent Neural Network , 2010, 2010 International Conference on Information Science and Applications.

[26]  Mirjana Ivanovic,et al.  A Framework for Time-Series Analysis , 2010, AIMSA.

[27]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[28]  Zhiping Lin,et al.  Predicting time series with wavelet packet neural networks , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).