Application of Multilayer Perceptron for Forecasting of Selected IIPs of India An Empirical Analysis

Das, Dipankar; Tripathi, Awanish Kumar; Shah, Ambika Prasad; Mehta, Samarth

doi:10.26438/ijcse/v6i11.400406

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2019

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“…The tool is capable of selecting the important and interpretable characteristics of the time series data [14]. Das et al (2018) had used MLP to develop forecast models for the 23 IIPs of India [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Extraction and Clustering of Twenty Three IIPs of India

Das¹,

Mukherjee²,

Roy³

et al. 2019

IJRAT

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The study analyzes the 'Index of Industrial Production' of twenty three (23) different items of India by extracting the trend, spike, curvature, linearity and entropy features of each of the twenty three (23) indexes. The authors also cluster this time series data on the basis of five (5) extracted features. The largest clusters of indexes were exhibited by both the linearity and spike features. For each of them i.e. linearity and spike features, the larger cluster contains nineteen ( 19) indexes. Outlier clusters were also identified in the cases of trend, linearity, spike and curvature features. The cluster analysis of the indexes together on spike and linearity gave us a single cluster with twenty (20) indexes while three (3) indexes were marked as outlier cluster. Index Terms-Feature extraction; Clustering; Time Series; IIP.

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Section: Literature Reviewmentioning

confidence: 99%