Monika Chuchro
Time Series Regression with MetaClusters
941 - 944
2013
7
12
International Journal of Mathematical and Computational Sciences
https://publications.waset.org/pdf/9996979
https://publications.waset.org/vol/84
World Academy of Science, Engineering and Technology
This paper presents a preliminary attempt to apply classification of time series using metaclusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain subgroups of time series data with normal distribution from the inflow into wastewater treatment plant data, composed of several groups differing by mean value. Two simple algorithms, Kmean and EM, were chosen as a clustering method. The Rand index was used to measure the similarity. After simple metaclustering, a regression model was performed for each subgroups. The final model was a sum of the subgroups models. The quality of the obtained model was compared with the regression model made using the same explanatory variables, but with no clustering of data. Results were compared using determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on a linear chart. Preliminary results allow us to foresee the potential of the presented technique.
Open Science Index 84, 2013