Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
On Similarity-Based Queries for Time Series Data
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Query cost estimation through remote system contention states analysis over the Internet
Web Intelligence and Agent Systems
Assisting decision making in the event-driven enterprise using wavelets
Decision Support Systems
Improving data reduction for 3D shape preserving
Journal of Computational Methods in Sciences and Engineering
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In this paper we introduce a modification of the real discrete Fourier transform and its inverse transform to filter noise and perform reduction on the data whilst preserving the trend of global moving of time series. The transformed data is still in the same time domain as the original data, and can therefore be directly used by any other mining algorithms.We also present a classification algorithm MinCov in this paper. Given a new data tuple, it provides values for each class that measures the likelihood of the tuple belonging to that class. The experimental results show that the MinCov algorithm is comparable to C4.5, and using MinCov as a mining algorithm the average hit rate of predicting the sign of stock return is 23.92% higher than that on the original data. This means that the predicting accuracy has been remarkably improved by means of the proposed data reduction and noise filtering method.