PMEM: Predicting multiple time series using ensemble model

Vishwanath Hulipalled, R. and Srikantaiah, K.C. and Venugopal, K.R. and Patnaik, L.M. (2016) PMEM: Predicting multiple time series using ensemble model. 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I). pp. 468-473.

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Official URL: https://10.1109/IC3I.2016.7918010


Forecasting Multiple Time Series (MTS) consists of multiple time series with no relation between them and independent of each other. Predicting each time series independently may lead to increase in time and cost. In this paper, we formalize the problem of predicting the multiple time series together over a MTS database. The proposed framework addresses the following issues. First, it build the initial ensemble model for each time series by using a novel Ensemble approach thereby effectively reduce the data storage and time complexity, secondly, by using single ensemble engine for MTS we perform the three major task, i.e., the task of prediction, building new model, ensemble update and lastly predicting the samples by pattern sequence matching using well known Sliding window method. The computational cost for PMEM is O(C * N * (K+ nmatches)) where, nmatches is the Average number of patterns matched …

Item Type: Article
Uncontrolled Keywords: Time series analysis , Predictive models , Buildings , Pattern matching , Data models , Engines , Computational modeling
Subjects: Faculty of Engineering > Computer Science & Information Science Engineering
Divisions: University Visvesvarayya College of Engineering > Department of Computer Science and Information Science Engineering
Depositing User: Ms. Shwetha A C
Date Deposited: 12 Oct 2021 11:01
Last Modified: 12 Oct 2021 11:01

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