Dynamic programming algorithm optimization for spoken word recognition
Readings in speech recognition
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Mining Generalized Association Rules for Sequential and Path Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Motifs in Massive Time Series Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
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
Exact indexing of dynamic time warping
Knowledge and Information Systems
Motif Extraction and Protein Classification
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
StockMarket Forecasting Using Hidden Markov Model: A New Approach
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
A trie-based APRIORI implementation for mining frequent item sequences
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Stock time series pattern matching: Template-based vs. rule-based approaches
Engineering Applications of Artificial Intelligence
Discovering Significant Patterns
Machine Learning
Detecting time series motifs under uniform scaling
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Toward accurate dynamic time warping in linear time and space
Intelligent Data Analysis
Proceedings of the VLDB Endowment
A non-linear forecasting system for the Ebro River at Zaragoza, Spain
Environmental Modelling & Software
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
An Approach for Automatic Sleep Stage Scoring and Apnea-Hypopnea Detection
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Mining approximate motifs in time series
DS'06 Proceedings of the 9th international conference on Discovery Science
Protein sequence classification through relevant sequence mining and bayes classifiers
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Combining motif information and neural network for time series prediction
International Journal of Business Intelligence and Data Mining
| Hi-index | 0.00 |
This paper presents an approach for time series prediction using a Hidden Markov Model, which bases on inter-time-serial correlations. These correlations between time series of a given database are automatically discovered by hierarchically clustering motif-based time series representations, which can be used for the prediction of the future development of one time series on base of known values from the one and correlated time series. The functionality and the influence of the different parameters of the motif-based representation, the inter-time-serial correlation discovery and the prediction capability are evaluated on two large databases of river level measurements and stock data.