An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
A sequential pruning strategy for the selection of the number of states in hidden Markov models
Pattern Recognition Letters
A Model Selection Criterion for Classification: Application to HMM Topology Optimization
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
StockMarket Forecasting Using Hidden Markov Model: A New Approach
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
A fusion model of HMM, ANN and GA for stock market forecasting
Expert Systems with Applications: An International Journal
MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections
IEEE Transactions on Neural Networks
A new distance measure for hidden Markov models
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A method for determination on HMM distance threshold
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Short-term sales forecasting with change-point evaluation and pattern matching algorithms
Expert Systems with Applications: An International Journal
A sparse kernel algorithm for online time series data prediction
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
HMM (Hidden Markov model) has been used successfully to analyze various types of time series. To fit time series with HMM, the number of hidden states should be determined before learning other parameters, since it has great impact on the complexity and precision of the fitting HMM. However this becomes too difficult when there is not enough prior knowledge about the observed series, which will lead to the increasing mean error in prediction process. To overcome this shortcoming, a prediction algorithm PAAMS for time series based on adaptive model selection is proposed. In PAAMS, the model can be dynamically updated when the prediction mean error increases. During the update process, an automatic model selection method AMSA is applied to get the best hidden state number and other model parameters. The proposed method AMSA is based on clustering, in which the number of hidden states is considered as the number of clusters. The feasibility and effectiveness of proposed prediction algorithm are explained. Experiments on American stock price data set are done and the results show that the PAAMS algorithm can achieve higher precision than that of previous study on the same data sets based on fixed model techniques.