Neural Network Stock Price Prediction. Ski.clps.brown.edu/cogsim/Emergent_v5.3.2_manual.pdf. Stock Market Forecasting Research Based on Neural Network and Pattern Matching. BP Neural Networks is one of the most popular tools in the analysis of stock data.
Recent research activities in Pattern Matching indicate that Pattern Matching just simplify the complexity of stock trend prediction and provide a simple but effective way for the stock market prediction. This paper analysis the theory of BP Neural Networks and Pattern Matching, proposes a method for combining these two algorithms to establish a stock market forecasting system based on BP Neural Networks and Pattern Matching. This system overcomes the shortcomings of the local least in the Neural Networks forecasting system's objective function and Pattern Matching System's lack of stock changing probabilities, takes advantage of the unique strength in stock price forecasting of these two algorithms. Finally, test this system by analyzing and forecasting the Titan Oil's stock price. The stock prediction community. General parameter learning. Error Sum of Squares. Error Sum of Squares (SSE) SSE is the sum of the squared differences between each observation and its group's mean.
It can be used as a measure of variation within a cluster. If all cases within a cluster are identical the SSE would then be equal to 0. The formula for SSE is: Where n is the number of observations xi is the value of the ith observation and 0 is the mean of all the observations. Www.f.kth.se/~f98-kny/thesis.pdf.