Determination of the Starting Point in Time Series for Trend Detection Based on Overlapping Trend

1. Janusz, M. E., V. Venkatasubramanian. Automatic Generation of Qualitative Descriptions of Process Trends for Fault Detection and Diagnosis. – Eng. Appl. Artif. Intell., Vol. 4, 1991, No 5, pp. 329-339.10.1016/0952-1976(91)90001-MSearch in Google Scholar

2. Li, H., C. Guo. Survey of Feature Representations Andsimilarity Measurements in Time Series Data Mining. – Appl. Res. Comput., Vol. 30, 2013, No 5, pp. 1285-1291.Search in Google Scholar

3. Keogh, E., K. Chakrabarti, M. Pazzani, S. Mehrotra. Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. – Knowl. Inf. Syst., Vol. 3, 2001, No 3, pp. 263-286.10.1007/PL00011669Search in Google Scholar

4. Agrawal, R., C. Faloutsos, A. Swami. Efficient Similarity Search in Sequence Databases. – Springer, 1993.10.1007/3-540-57301-1_5Search in Google Scholar

5. Struzik, Z. R., A. Siebes. Wavelet Transform in Similarity Paradigm. – Springer, 1998.10.1007/3-540-64383-4_25Search in Google Scholar

6. Keogh, E., S. Chu, D. Hart, M. Pazzani. An Online Algorithm for Segmenting Time Series. – In: Proc. of IEEE International Conference on Data Mining 2001 (ICDM’01), IEEE, 2001, pp. 289-296.Search in Google Scholar

7. Korn, F., H. V. Jagadish, C. Faloutsos. Efficiently Supporting Ad Hoc Queries in Large Datasets of Time Sequences. – ACM SIGMOD Rec., Vol. 26, 1997, No 2, pp. 289-300.10.1145/253262.253332Search in Google Scholar

8. Li, A., Q. Zheng. Dimensionality Reduction and Similarity Search in Large Time Series Databases. – Chin. J. Comput., Vol. 28, 2005, No 9, pp. 1467-1475.Search in Google Scholar

9. Fuchs, E., T. Gruber, J. Nitschke, B. Sick. Online Segmentation of Time Series Based on Polynomial Least-Squares Approximations. – Pattern. Anal. Mach. Intell. IEEE Trans. On, Vol. 32, 2010, No 12, pp. 2232-2245.10.1109/TPAMI.2010.4420975120Search in Google Scholar

10. Xuedong, G., G. Kan. The Inertia Test and Trend Partition for Trend Detection in Sequential Data. – In: 2015 International Conference on Logistics, Informatics and Service Sciences (LISS’15). –IEEE, 2015, pp. 1-6.10.1109/LISS.2015.7369685Search in Google Scholar

11. Zhang, C., L. Huang, Z. Zhao. Research on Combination Forecast of Port Cargo Throughput Based on Time Series and Causality Analysis. – J. Ind. Eng. Manag., Vol. 6, 2013, No 1, pp. 124-134.10.3926/jiem.687Search in Google Scholar

12. Kuhn, J. Time Analysis in International Logistics Systems with the 6-Sigma Approach Towards an International JIT-System. – J. Syst. Manag. Sci., Vol. 1, 2011, No 1, pp. 73-81.Search in Google Scholar

13. He, S. Applied Time Series Analysis. Peking University Press, 2004.Search in Google Scholar

14. Fang, J. Time Series Piecewise Linear Representation and Qualitative Trend Analysis. Mastersthesis, Lanzhou University of Technology, 2013.Search in Google Scholar

15. Babcock, B., S. Babu, M. Datar, R. Motwani, J. Widom. Models and Issues in Data Stream Systems. – In: Proc. of 21 ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, ACM, 2002, pp. 1-16.10.1145/543613.543615Search in Google Scholar