A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting
Applied Intelligence
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Representing financial time series based on data point importance
Engineering Applications of Artificial Intelligence
An evolutionary approach to pattern-based time series segmentation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
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Many machine learning methods in Artificial intelligence literature, such as Neural Networks, Genetic Algorithms, SVM, and Case-Based Reasoning, have been applied to forecast the financial market with high irregularity and uncertatinty. Among them, SVM is the most representative method that models and forecasts financial time series. However, SVM cannot reflect on the dynamic characteristic of financial time series effectively due to its superior generalization performance. And we cannot guarantee that the parameters of SVM have the optimum values, since they are locally searched owing to the limited time bound. These vulnerabilities are the main factors that degenerates the forecasting performance of SVM. On the other hand, continuous HMM can effectively capture the irregular and dynamic movement of financial time series with a non-stationary property, since it models a financial time series stochastically, rather than deterministically. Therefore, this paper suggests a new method that constructs the trend forecasting model of financial time series. It firstly detects PIPs indicating the significant turnabout of trend in each financial time series. And then the detected PIPs are used to construtct its trend forecasting model based on continuous HMM. In the experiment with various financial time series datasets we demonstrate its superiority.