SYMBOLIC CLASSIFICATION FOR MULTIVARIATE TIME SERIES.
- Btech (CSE), GTBIT, New Delhi.
- Ast. Professor(CSE/IT), GTBIT, New Delhi.
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With emergence of various marketing strategies, there is a need of efficient ways to collaborate all the information pertaining to our domain and hence find useful trends, patterns and its associations. To do the same we use multivariate time series, where we concatenate a number of time series pertaining to a single domain. Consumption and supply are such examples to study the trends and patterns of the market, we also need to classify the human resources to identify our potential customers. This project is a brief comparison of the classification algorithms such as random forest and Support vector machines applied on a multivariate time series, it focusses at comparing the error rate of the above stated algorithm for different sizes of dataset, so that one can efficiently choose an algorithm and classification when wanting to study a multivariate time series.
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[Amanpreet Singh, Dashmeet Kaur Sethi, Karneet Singh, Lakshay Sharma and Poonam Narang. (2017); SYMBOLIC CLASSIFICATION FOR MULTIVARIATE TIME SERIES. Int. J. of Adv. Res. 5 (May). 1982-1987] (ISSN 2320-5407). www.journalijar.com
Btech (CSE)
