Finding motifs using random projections
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Efficient Pattern Matching of Time Series Data
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications (Advances in Industrial Control)
Detecting time series motifs under uniform scaling
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Quarterly Time-Series Forecasting With Neural Networks
IEEE Transactions on Neural Networks
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Recent research works pay more attention to time series prediction, in which some time series data mining approaches have been exploited. In this paper, we propose a new method for time series prediction which is based on the concept of time series motifs. Time series motif is a previously unknown pattern appearing frequently in a time series. In the proposed approach, we first search for time series motif by using EP-C algorithm and then exploit motif information for forecasting in combination of a neural network model. Experimental results demonstrate our proposed method performs better than artificial neural network ANN in terms of prediction accuracy and time efficiency. Besides, our proposed method is more robust to noise than ANN.