{"title":"Representing Data without Lost Compression Properties in Time Series: A Review","authors":"Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan","volume":84,"journal":"International Journal of Computer and Information Engineering","pagesStart":1579,"pagesEnd":1583,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9996863","abstract":"
Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.<\/p>\r\n","references":"[1]\tS. P. Lykoudis, A. a. Argiriou, and E. Dotsika, \"Spatially interpolated time series of \u03b418\u039f in Eastern Mediterranean precipitation,\u201d Glob. Planet. Change, vol. 71, no. 3\u20134, pp. 150\u2013159, Apr. 2010.\r\n[2]\tH. L. Cloke and F. Pappenberger, \"Ensemble flood forecasting: A review,\u201d J. Hydrol., vol. 375, no. 3\u20134, pp. 613\u2013626, Sep. 2009.\r\n[3]\tM. Hooshsadat and O. R. Za, \"An Associative Classifier For Uncertain Datasets,\u201d in in Advances in Knowledge Discovery and Data Mining, 2012, p. pp 342\u2013353.\r\n[4]\tV. Jankovic, \"Science Migrations: Mesoscale Weather Prediction from Belgrade to Washington, 1970\u20132000,\u201d Soc. Stud. Sci., vol. 34, no. 1, pp. 45\u201375, Feb. 2004.\r\n[5]\tD. J. Gagne, A. McGovern, and M. Xue, \"Machine learning enhancement of storm scale ensemble precipitation forecasts,\u201d in Proceedings of the 2011 workshop on Knowledge discovery, modeling and simulation - KDMS \u201911, 2011, p. 45.\r\n[6]\tT. Fu, \"A review on time series data mining,\u201d Eng. Appl. Artif. Intell., vol. 24, no. 1, pp. 164\u2013181, Feb. 2011.\r\n[7]\tJ. A\u00dffalg, H. Kriegel, P. Kr, and M. Renz, \"Probabilistic Similarity Search for Uncertain Time Series,\u201d in SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management, 2009, pp. 435 \u2013 443.\r\n[8]\tS. R. Sarangi and K. Murthy, \"DUST: A Generalized Notion of Similarity between Uncertain Time Series Similarity of Uncertain Time Series,\u201d IBM India Res. Lab, vol. 1, 2010.\r\n[9]\tC. Qing, Z. Xiaoli, and Z. Kun, \"Research on Precipitation Prediction Based on Time Series Model,\u201d in 2012 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring, 2012, pp. 568\u2013571.\r\n[10]\tS. Esfandeh and M. Sedighizadeh, \"Meteorological Data Study and Forecasting Using Particle Swarm Optimization Algorithm,\u201d World Acad. Sci. Eng. Technol., vol. 59, pp. 2117\u20132119, 2011.\r\n[11]\tZ. Nan, S. Wang, X. Liang, T. E. Adams, W. Teng, Y. Liang, and S. Member, \"Analysis of Spatial Similarities Between NEXRAD and NLDAS Precipitation Data Products,\u201d IEEE J. Sel. Top. Appl. EARTH Obs. Remote Sens., vol. 3, no. 3, pp. 371\u2013385, 2010.\r\n[12]\tC. M. Sadler and M. Martonosi, \"Data compression algorithms for energy-constrained devices in delay tolerant networks,\u201d in Proceedings of the 4th international conference on Embedded networked sensor systems - SenSys \u201906, 2006, p. 265.\r\n[13]\tK. C. Barr and K. Asanovi\u0107, \"Energy-aware lossless data compression,\u201d ACM Trans. Comput. Syst., vol. 24, no. 3, pp. 250\u2013291, Aug. 2006.\r\n[14]\tA. et al., \"System and Method for Differential Compression of Data from A Plurality of Binary Sources,\u201d U.S.Patent, vol. 2, no. 12, pp. 1\u201335, 2002.\r\n[15]\tLevine, \"Lossless Data Compression with Low Complexity,\u201d United States Pat., pp. 1\u201325, 2000.\r\n[16]\tS. Member and S. Member, \"Power Quality Disturbance Data Compression using Wavelet Transform Methods,\u201d IEEE Trans. Power Deliv., vol. 12, no. 3, pp. 1250\u20131257, 1997.\r\n[17]\tA. F. Heavens, R. Jimenez, and O. Lahav, \"Massive lossless data compression and multiple parameter estimation from galaxy spectra,\u201d Mon. Not. R. Astron. Soc., vol. 317, no. 4, pp. 965\u2013972, Oct. 2000.\r\n[18]\tJ. Shanmugasundaram, U. Fayyad, and P. S. Bradley, \"Compressed data cubes for OLAP aggregate query approximation on continuous dimensions,\u201d in Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD \u201999, 1999, pp. 223\u2013232.\r\n[19]\tM. Poess, \"Data Compression in Oracle,\u201d in Proceedings of the 29th VLDB Conference, Berlin, Germany, 2003.\r\n[20]\tT. A. Welch, \"A Technique for Hogh-Performance Data Compression,\u201d IEEE Comput., pp. 8\u201320, 1984.\r\n[21]\tE. H. Volkerink, A. Khoche, and S. Mitra, \"Packet-based input test data compression techniques,\u201d in Proceedings. International Test Conference, 2002, pp. 154\u2013163.\r\n[22]\tB. Ryabko, J. Astola, and M. Malyutov, Compression-Based Methods of Prediction and Statistical Analysis of Time Series: Theory and Applications. 2010, pp. 1\u2013109. \r\n","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 84, 2013"}