Abstract
In recent years, there has been an explosion of problems concerned with mining time series databases and dimensionality reduction is one of the important tasks in time series data mining analysis. These approaches are very useful to pre-process the large dataset and then use it to analyze and mine. In this paper, we propose a method based on turning points to reduce the dimensions of stream time series data, this task helps the prediction process faster. The turning points which are extracted from the maximum or minimum points of the time series stream are proved more efficient and effective in preprocessing data for stream time series prediction. To implement the proposed framework, we use stock time series obtained from Yahoo Finance, the prediction approach based on Sequential Minimal Optimization and the experimental results validate the effectiveness of our approach.
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Vo, V., Jiawei, L., Vo, B. (2013). Dimensionality Reduction by Turning Points for Stream Time Series Prediction. In: Nguyen, N., Trawiński, B., Katarzyniak, R., Jo, GS. (eds) Advanced Methods for Computational Collective Intelligence. Studies in Computational Intelligence, vol 457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34300-1_16
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DOI: https://doi.org/10.1007/978-3-642-34300-1_16
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