Stock price prediction is the most difficult field due to irregularity. However, because stock price is sometimes showing similar patterns and is determined by a variety of factors, our new idea is to find similar patterns in historical stock data to achieve daily stock price with high prediction accuracy and potential rules selecting main factors that have significant effect on the price among all factors simultaneously. The goal of our paper is to suggest a new complex methodology that finds the optimal historical dataset with similar patterns according to various algorithms for each stock item and provides a more accurate prediction of daily stock price. First, we use a Dynamic Time Warping algorithm to find patterns with the most closely similar situation adjacent to a current pattern. Second, we select the determinant that are most influenced by the stock price using feature selection based on Stepwise Regression Analysis. Moreover, we generate an artificial neural network model with selected features as training data for predicting the best stock price. Finally, we use Jaro-Winkler distance with Symbolic Aggregate approXimation (SAX) as prediction accuracy measure to verify our model.
The method of real-time estimation of weather, especially the amount of rainfall, by analyzing CCTV images is much cheaper than one using the existing expensive weather observation equipment. In this paper, we propose a method to find an estimation model function which has its input as CCTV images and output as the amount of rainfall. From the CCTV images, we propose an algorithm for selecting the number and size of the region of interest optimized for rainfall estimation, generating a data pattern graph showing a clear distinction from the number of region of interest, clustering the pattern data graphs, and estimating the amount of rainfall. Experiments using real CCTV images show that the estimation accuracy is over 80%.
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