# Improving prediction models applied in systems monitoring natural hazards and machinery

International Journal of Applied Mathematics and Computer Science (2012)

- Volume: 22, Issue: 2, page 477-491
- ISSN: 1641-876X

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topMarek Sikora, and Beata Sikora. "Improving prediction models applied in systems monitoring natural hazards and machinery." International Journal of Applied Mathematics and Computer Science 22.2 (2012): 477-491. <http://eudml.org/doc/208123>.

@article{MarekSikora2012,

abstract = {A method of combining three analytic techniques including regression rule induction, the k-nearest neighbors method and time series forecasting by means of the ARIMA methodology is presented. A decrease in the forecasting error while solving problems that concern natural hazards and machinery monitoring in coal mines was the main objective of the combined application of these techniques. The M5 algorithm was applied as a basic method of developing prediction models. In spite of an intensive development of regression rule induction algorithms and fuzzy-neural systems, the M5 algorithm is still characterized by the generalization ability and unbeatable time of data model creation competitive with other systems. In the paper, two solutions designed to decrease the mean square error of the obtained rules are presented. One consists in introducing into a set of conditional variables the so-called meta-variable (an analogy to constructive induction) whose values are determined by an autoregressive or the ARIMA model. The other shows that limitation of a data set on which the M5 algorithm operates by the k-nearest neighbor method can also lead to error decreasing. Moreover, three application examples of the presented solutions for data collected by systems of natural hazards and machinery monitoring in coal mines are described. In Appendix, results of several benchmark data sets analyses are given as a supplement of the presented results.},

author = {Marek Sikora, Beata Sikora},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {natural hazards monitoring; regression rules; time series forecasting; k-nearest neighbors; -nearest neighbors},

language = {eng},

number = {2},

pages = {477-491},

title = {Improving prediction models applied in systems monitoring natural hazards and machinery},

url = {http://eudml.org/doc/208123},

volume = {22},

year = {2012},

}

TY - JOUR

AU - Marek Sikora

AU - Beata Sikora

TI - Improving prediction models applied in systems monitoring natural hazards and machinery

JO - International Journal of Applied Mathematics and Computer Science

PY - 2012

VL - 22

IS - 2

SP - 477

EP - 491

AB - A method of combining three analytic techniques including regression rule induction, the k-nearest neighbors method and time series forecasting by means of the ARIMA methodology is presented. A decrease in the forecasting error while solving problems that concern natural hazards and machinery monitoring in coal mines was the main objective of the combined application of these techniques. The M5 algorithm was applied as a basic method of developing prediction models. In spite of an intensive development of regression rule induction algorithms and fuzzy-neural systems, the M5 algorithm is still characterized by the generalization ability and unbeatable time of data model creation competitive with other systems. In the paper, two solutions designed to decrease the mean square error of the obtained rules are presented. One consists in introducing into a set of conditional variables the so-called meta-variable (an analogy to constructive induction) whose values are determined by an autoregressive or the ARIMA model. The other shows that limitation of a data set on which the M5 algorithm operates by the k-nearest neighbor method can also lead to error decreasing. Moreover, three application examples of the presented solutions for data collected by systems of natural hazards and machinery monitoring in coal mines are described. In Appendix, results of several benchmark data sets analyses are given as a supplement of the presented results.

LA - eng

KW - natural hazards monitoring; regression rules; time series forecasting; k-nearest neighbors; -nearest neighbors

UR - http://eudml.org/doc/208123

ER -

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