The visual display of quantitative information
The visual display of quantitative information
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Similarity-based queries for time series data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
IEEE Transactions on Knowledge and Data Engineering
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
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
Subsequence matching on structured time series data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Clustering of time-series subsequences is meaningless: implications for previous and future research
Knowledge and Information Systems
Continuous Similarity-Based Queries on Streaming Time Series
IEEE Transactions on Knowledge and Data Engineering
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Capability and limitation of financial time-series data prediction using symbol string quantization
Proceedings of the 2009 International Conference on Hybrid Information Technology
International Journal of Data Analysis Techniques and Strategies
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Time series data poses a significant variation to the traditional segmentation techniques of data mining because the observation is derived from multiple instances of the same underlying record. Additionally, the standard segmentation methods employed in traditional clustering require instances to be classified exactly by attaching an event to a specific cluster at the exclusion of other clusters. This paper is an investigation into the predictive power of the clustering technique on stock market data and its ability to provide stock predictions that can be utilised in strategies that outperform the underlying market. This uses a brute force approach to the prediction of stock prices based on the formation of a cluster around the query sequence. The prediction is then applied in a model designed to capitalise on the derived prediction. The predictive accuracy of minimum distance clusters produced promising results with a prediction error incorporated into the forecast strategy.