Juxtaposition of Different Machine Learning Techniques for Improved Time Series Classification
Ishan Yash1, Hemprasad Yashwant Patil2, Usha Rani Seshasayee3

1AIshan Yash, Electronics and Communications Engineering with Specialization in IoT and Sensors, VIT Vellore, Tamil Nadu, India.
2Dr. Hemprasad Yashwant Patil, Department of Embedded Technology, School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, India.
3Dr. Usha Rani Seshasayee, Department of Embedded Technology, School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, India.

Manuscript received on October 15, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 9-17 | Volume-9 Issue-1, November 2019. | Retrieval Number: A3887119119/2019©BEIESP | DOI: 10.35940/ijitee.A3887.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: An essential type of TS analysis is classification, which can, for instance, advance energy load forecasting in smart grids by discovering the varieties of electronic gadgets based totally on their strength expenditure profiles recorded by way of computerized sensors. Such applications are very often characterised by using (a) very lengthy TS and (b) extensive TS datasets needing classification. but, current techniques to time series classification (TSC) cannot deal with such facts volumes at desirable accuracy. WEASEL (Word ExtrAction for time SEries cLassification), a novel TSC method which is each rapid and unique. Like different today’s TSC techniques, WEASEL modifies time collection into characteristic vectors, the use of a sliding-window approach, which is then surpassed via a device getting to know classifier. Our approach here is the amalgamation of Distance-specific approaches such as DTW alongwith feature-specific approaches namely SAX and WEASEL and hence, this method may be effortlessly prolonged to be used in aggregate with different strategies. specially, we show that once blended with the space measures which include Minkowski distance measures, DTW, SAX and PAA, it outperforms the previously known methods.
Keywords: WEASEL, Symbolic Aggregate Approximation (SAX), Piecewise Aggregate Approximation (PAA), Distance-Time Warping(DTW)
Scope of the Article: Approximation and Randomized Algorithms