JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Efficient Anomaly Detection Through Confidence Interval Estimation Based on Time Series Analysis
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Efficient Anomaly Detection Through Confidence Interval Estimation Based on Time Series Analysis
Kim, Yeong-Ju; Jeong, Min-A;
  PDF(new window)
 Abstract
This paper suggests a method of real time confidence interval estimation to detect abnormal states of sensor data. For real time confidence interval estimation, the mean square errors of the exponential smoothing method and moving average method, two of the time series analysis method, were compared, and the moving average method with less errors was applied. When the sensor data passes the bounds of the confidence interval estimation, the administrator is notified through alarms. As the suggested method is for real time anomaly detection in a ship, an Android terminal was adopted for better communication between the wireless sensor network and users. For safe navigation, an administrator can make decisions promptly and accurately upon emergency situation in a ship by referring to the anomaly detection information through real time confidence interval estimation.
 Keywords
nautical safety;time series analysis;moving average method;exponential smoothing;
 Language
English
 Cited by
 References
1.
J. H. Park, B. T. Jang, and D. S. Lim, "Safe operation of the shipyard and ship building digital technology developments supported," Korean Inst. Inf. Sci. Eng.(KIISE), Vol. 31, No. 1, pp. 55-63, Jan. 2003.

2.
A. C. Harvey, Time Series Models, 2nd Ed., MIT Press, (308), 1993.

3.
H. Zou and Y. H. Yang, "Combining Time Series Model for Forecasting," Int. J. Forecasting, Vol. 20, No. 1, pp. 69-84, 2004 crossref(new window)

4.
K. H. Cho and D. H. Lee, "A Study on Traffic Anomaly Detection Scheme Based Time Series Model," J. KICS, Vol. 33, No. 5, pp. 304-309, 2008.

5.
H. G. No, SPSS / Excel by time series analysis, HYOSAN, (323), 2008.

6.
Lim, M. Michael, "Time Series Forecasts of International Travel Demand for Australia," Tourism Management, Vol. 23, No. 4, pp. 389-396, Aug. 2002. crossref(new window)

7.
Y. H. Kim, Time Series Prediction, HSPN, (448), 2002.

8.
E. H. Kim, S. H. Lee, Teaching Statistics, KYUNGMOON, (270), 2007.

9.
P. Buonadonna, D. Gay, J. M. Hellerstein, W. Hong, and S. Madden, "Task: Sensor network in a box," in Proc. 2nd European Workshop on Wirel. Sensor Netw., pp. 133-144, Istanbul, Turkey, Feb. 2005.

10.
G. Werner-Allen, K. Lorincz, J. Johnson, J. Lees, and M. Welsh, "Fidelity and yield in a volcano monitoring sensor network," in Proc. 7th USENIX Symp. Operating System Design and Implementation, pp. 381-396, Berkeley, USA, Nov. 2006.

11.
K. Ni, N. Ramanathan, M. N. H. Chehade, L. Bal zano, S. Nair, S. Zahedi, E. Kohler, G. Pottie, M. Hansen, and M. Srivastava, "Sensor Network Data Fault Types," J. ACM Trans. Sensor Netw., Vol. 5, No. 3, pp. 1-29, Aug. 2009.

12.
E. Elnahrawy and B. Nath, "Cleaning and Querying Noisy Sensors," in Proc. Int. Workshop Wirel. Sensor Netw. Appl., pp. 78-87, New York, USA, Sept. 2003.

13.
S. R. Jeffery, G. Alonso, M. J. Franklin, W. Hong, and J. Widom. "Declarative support for sensor data cleaning," in Proc. Int. Conf. Pervasive Computing, Lecture Notes in Comput. Sci., Vol. 3968, pp. 83-100, Dublin, Ireland, May 2006.

14.
G. Shmueli, N. R. Patel, and P. C. Bruce, TData Mining for Business Intelligence, E&B, (460), 2006.

15.
S. D. Lee and U. R. Lee, Time series data analysis using SAS, TAMJI, (319), 2006.