Higher-order Markov chain models for categorical data sequences. (English) Zbl 1054.62098
Summary: We study higher-order Markov chain models for analyzing categorical data sequences. We propose an efficient estimation method for the model parameters. Data sequences such as DNA and sale demands are used to illustrate the predicting power of our proposed models. In particular, we apply the developed higher-order Markov chain model to the server log data. The objective here is to model the users’ behavior in accessing information and to predict their behavior in the future. Our tests are based on a realistic web log and our model shows an improvement in prediction.
MSC:
| 62M05 | Markov processes: estimation; hidden Markov models |
| 90C08 | Special problems of linear programming (transportation, multi-index, data envelopment analysis, etc.) |
| 60J10 | Markov chains (discrete-time Markov processes on discrete state spaces) |
| 62P99 | Applications of statistics |
Software:
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