Abstract
Trend analysis of time series data is an important research direction. In streaming time series the problem is more challenging, taking into account the fact that new values arrive for the series, probably in very high rates. Therefore, effective and efficient methods are required in order to classify a streaming time series based on its trend. Since new values are continuously arrive for each stream, the classification is performed by means of a sliding window which focuses on the last values of each stream. Each streaming time series is transformed to a vector by means of a Piecewise Linear Approximation (PLA) technique. The PLA vector is a sequence of symbols denoting the trend of the series (either UP or DOWN), and it is constructed incrementally. Efficient in-memory methods are used in order to: 1) determine the class of each streaming time series and 2) determine the streaming time series that comprise a specific trend class. Performance evaluation based on real-life datasets is performed, which shows the efficiency of the proposed approach both with respect to classification time and storage requirements. The proposed method can be used in order to continuously classify a set of streaming time series according to their trends, to monitor the behavior of a set of streams and to monitor the contents of a set of trend classes.
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Aggarwal, C.C., Han, J., Yu, P.S.: On Demand Classification of Data Streams. In: Proceedings of the International Conference of Knowledge Discovery and Data Mining(KDD), WA, USA (2004)
Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and Issues in Data Stream Systems. In: Proceedings ACM PODS, Madison, Wisconsin, pp. 1–16 (2002)
Datar, M., Gionis, A., Indyk, P., Motwani, R.: Maintaining stream statistics over sliding windows. In: Proceedings of the 2002 Annual ACM-SIAM Symp. on Discrete Algorithms, pp. 635–644 (2002)
Domingos, P., Hulten, G.: Mining High-Speed Data Streams. In: Proceedings of ACM SIGKDD Conference (2000)
Fung, G.P.C., Yu, J.X., Lam, W.: News Sensitive Stock Trend Prediction. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 481–493. Springer, Heidelberg (2002)
Guha, S., Meyerson, A., Mishra, N., Motwani, R., OCallaghan, L.: Clustering Data Streams: Theory and Practic. IEEE TKDE 15(3), 515–528 (2003)
Guha, S., Mishra, N., Motwani, R., OÆCallaghan, L.: Clustering data streams. In: Proc. of the 2000 Annual IEEE Symp. on Foundations of Computer Science, pp. 359–366 (2000)
Hulten, G., Spencer, L., Domingos, P.: Mining Time Changing Data Streams. In: Proceedings of ACM KDD Conference (2001)
Hutson, J.K.: TRIX - Triple Exponential Smoothing Oscillator. Technical Analysis of Stocks and Commodities, 105–108 (1983)
Keogh, E., Chakrabarti, K., Mehrotra, S., Pazzani, M.: Locally Dimensionality Reduction for Indexing Large Time Series Databases. In: Proceedings of ACM SIGMOD Conference, California, USA (2001)
Keogh, E., Pazzani, M.: A simple dimensionality reduction technique for fast similarity search in large time series databases. In: Proceedings of Pacific- Asia Conf. on Knowledge Discovery and Data Mining, pp. 122–133 (2000)
Last, M.: Online Classification of Nonstationary Data Streams. Intelligent Data Analysis 6(2), 129–147 (2002)
Ljubic, P., Todorovski, L., Lavrac, N., Bullas, J.C.: Time-series analysis of UK traffic accident data. In: Proceedings of the Conference on Data Mining and WareHouses (SiKDD), Ljubljana, Slovenia (2002)
Sacchi, L., Bellazzi, R., Larizza, C., Magni, P., Curk, T., Petrovic, U., Zupan, B.: Clustering and Classifying Gene Expressions Data through Temporal Abstractions. In: Proceedings of 8th Intelligence Data Analysis in Medicine and Pharmacology Workshop(IDAMAP 2003), Protaras, Cyprus (2003)
Takada, T., Kurihara, S., Hirotsu, T., Sugawara, T.: Proximity Mining: Finding Proximity using sensor Data History. In: Proceedings of 5th IEEE Workshop on Mobile Computing Systems and Applications (WMCSA), CA, USA (2003)
Wu, H., Salzberg, B., Zhang, D.: Online Event-driven Subsequence Matching over Financial Data Streams. In: Proceedings of ACM SIGMOD Conference, Paris, France (2004)
Yi, B.-K., Faloutsos, C.: Fast Time Sequence Indexing for Arbitrary Lp Norms. In: Proceedings of 26th International Conference on Very Large Databases (VLDB), Cairo, Egypt (2000)
Yoon, J.P., Luo, Y., Nam, J.: A Bitmap Approach to Trend Clustering for Prediction in Time-Series Databases. In: Proceedings of Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, Florida, USA (2001)
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Kontaki, M., Papadopoulos, A.N., Manolopoulos, Y. (2005). Continuous Trend-Based Classification of Streaming Time Series. In: Eder, J., Haav, HM., Kalja, A., Penjam, J. (eds) Advances in Databases and Information Systems. ADBIS 2005. Lecture Notes in Computer Science, vol 3631. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11547686_22
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DOI: https://doi.org/10.1007/11547686_22
Publisher Name: Springer, Berlin, Heidelberg
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