A TURNING POINTS METHOD FOR STREAM TIME SERIES PREDICTION

All of dimensionality reduction techniques are very meaningful to preprocess the large dataset and then use it to analyze and discover knowledge. In this paper, we propose an approach established on turning points to reduce the dimensions of stream time series data, and this task supports the prediction process faster in stream data environment. The turning points that are extracted from the maximum or minimum points of the time series data proved more efficient and effective in the process of preprocessing data for time series predictive analysis. To execute the proposed framework, we apply the stock time series obtained from Yahoo Finance, the predictive analysis based on a Sequential Minimal Optimization algorithm and the experimental results validate the effectiveness of our approach.