Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 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
Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping
IEEE Transactions on Knowledge and Data Engineering
Classification of multivariate time series using locality preserving projections
Knowledge-Based Systems
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Feature extraction for time series classification using discriminating wavelet coefficients
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Feature selection for classification of oscillating time series
Expert Systems: The Journal of Knowledge Engineering
| Hi-index | 0.01 |
Time-series classification has attracted increasing interest in recent years, particularly for long time-series as those arising in bioinformatics and financial domain. Many dimensionality reduction algorithms have been proposed to attack the so-called curse of dimensionality problem. However, choosing the number of features is not a trivial task and has not been well considered. In this paper, we propose a novel blind feature extraction algorithm with Haar wavelet transform which can determine the feature dimensionality automatically. The algorithm takes the tradeoff of achieving lower dimensionality and lower sum of squared errors between the features and original time-series. Experimental results performed on several widely used time-series data demonstrate the effectiveness of the proposed algorithm