Foundations of statistical natural language processing
Foundations of statistical natural language processing
Machine Learning
Data streams: algorithms and applications
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
StockMarket Forecasting Using Hidden Markov Model: A New Approach
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Research issues in data stream association rule mining
ACM SIGMOD Record
Processing forecasting queries
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Identifying event sequences using hidden Markov model
NLDB'07 Proceedings of the 12th international conference on Applications of Natural Language to Information Systems
Topology estimation of hierarchical hidden Markov models for language models
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Data stream forecasting for system fault prediction
Computers and Industrial Engineering
Towards the detection of unusual temporal events during activities using HMMs
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
| Hi-index | 0.00 |
In this paper, we propose a new technique for time-series prediction. Here we assume that time-series data occur depending on event which is unobserved directly, and we estimate future data as output from the most likely event which will happen at the time. In this investigation we model time-series based on event sequence by using Hidden Markov Model (HMM), and extract time-series patterns as trained HMM parameters. However, we can't apply HMM approach to data stream prediction in a straightforward manner. This is because Baum-Welch algorithm, which is traditional unsupervised HMM training algorithm, requires many stored historical data and scan it many times. Here we apply incremental Baum-Welch algorithm which is an on-line HMM training method, and estimate HMM parameters dynamically to adapt new time-series patterns. And we show some experimental results to see the validity of our method.