Autoregressive model learning device for time-series data...

Data processing: artificial intelligence – Machine learning

Reexamination Certificate

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C706S012000, C706S014000, C706S016000, C706S045000, C706S050000, C708S003000, C708S100000, C708S131000, C708S160000, C708S446000, C708S800000

Reexamination Certificate

active

07346593

ABSTRACT:
For sequentially input data string, the outliner and the change point are detected through calculation of the outlier score and the change point score by combining a time-series model learning device to learn the generation mechanism of the read data series as the time-series statistic model, a score calculator to calculate the outlier score of each data based on the time-series model parameter and the input data, a moving average calculator to calculate the moving average of the outlier score, a time-series model learning device to learn the generation mechanism of the moving average series as the time-series statistic model and the above score calculator that further calculates the outlier score of the moving average based on the moving average of the outlier score and outputs the result as the change point score of the original data.

REFERENCES:
patent: 5257364 (1993-10-01), Melamed et al.
patent: 6055491 (2000-04-01), Biliris et al.
patent: 6697769 (2004-02-01), Goodman et al.
patent: 2361336 (2001-10-01), None
patent: 2001-101154 (2001-04-01), None
Shaw et al. “Automated Error Detection in Multibeam Bathymetry Data”, IEEE ublication, 1993.
Burge et al “Detecting Cellular Fraud Using Adaptive Prototypes”.
www.trade10.com, “moving average”.
Li et al., “GPS Time Series Modeling by Autoregressive Moving Average Method: Application to the Crustal Deformation in Central Japan”, The Society of Geomagnetism and Earth, Planetary and Space Sciences, 2000, pp. 155-162.
Li et al. “GPS Time Series Modelling by Autoregressive Moving Average Method Application to the Crustal Deformation in Central Japan”, The Society of Geomagnetism and Earth, Planetary and Space Sciences, 2000, pp. 155-161.
Li et al. “GPS Time Series Modeling by Autoregressive Moving Average Method: Application to the Crustal Deformation in Central Japan”, 2000, pp. 155-162.
P. Burge, et al., “Detecting Cellular Fraud Using Adaptive Prototypes.”, (Proceedings of Al Approaches to Fraud Detection and Risk Management), pp. 9-13, 1997.
K. Yamanishi, et al., “On-line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms”, (Proceedings of the sixth ACM SIGKDD International Conference onKnwoledge Discovery and Data Mining, ACM Press), pp. 320-324, 2000.
U. Murad, et al., “Unsupervised Profiling for Identifying Superimposed Fraud”, (Proceedings of 3rdEuropean conference on Principles and Practice of Knowledge Discovery in Database), pp. 251-261, 1999.
B. Guthery, “Partition Regression”, in Journal of American Statistical Association, vol. 69, pp. 945-947, 1974.
M. Huskova, “Nonparametric Procedures for Detecting a Change in Simple Linear Regression Models”,Applied Change Point Problems in Statistics, Nova Science Publishers, Inc., 1995.
V. Guralnik, et al., “Event Detection from Time Series Data”, (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press), pp. 32-42, 1999.
Vasundhara Puttagunta, et al., Adaptive Methods for Activity Monitoring of Streaming Data, Proceedings of the 2002 International Conferences on Machine Learning and Applications (ICMLA '02), U.S.A., CSREA Press, Jun. 24, 2002, pp. 197-203, [Searched Feb. 2, 2006], Internet <URL, http://www.csee.umbc.edu/—kalpakis/homepage/papers/kalpakisICMLA02.pdf>.
Kenji Yamanishi et al., “Data Mining with Statistic Outlier Detection and Application to Network Invasion Detection,” Technical Research Reports of the Institute of Electronics, Information and Communication Engineers, Japan, the Institute of Electronics, Information and Communication Engineers, Jun. 14, 2002, vol. 102, No. 132, pp. 19-24.
Ikuo Yonemoto et al., “On-line Estimation of Probability Density Function Using Information Quantity Standard,” Technical Research Reports of the Institute of Electronics, Information and Communication Engineers, Japan, the Institute of Electronics, Information and Communication Engineers, Dec. 18, 1998, vol. 98, No. 490, pp. 189-194.

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