Evaluating and Forecasting rate of Counterfeit Banknote Detection
Akanksha Upadhyaya1, Vinod Shokeen2, Garima Srivastava3 

1Akanksha Upadhyaya, AIIT, Amity University, Noida, (RDIAS), India.
2Vinod Shokeen, AIARS, Amity University, Noida, India.
3Garima Srivastava, CS/IT, DRKNMIET, Ghaziabad, India.

Manuscript received on 10 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 5724-5731 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3381078219/2019©BEIESP | DOI: 10.35940/ijrte.B3381.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Detection of counterfeit banknotes becoming a major issue in today’s scenario, as it is seems to be weighty aspect in terms of socio-economy of every living being. This paper highlights various distinguishing features of currencies of different countries, having an objective to secure their currency less susceptible from forgery. Afterwards, the paper focuses on analysing and comparing the rate of counterfeit detection with the forecasted rate of counterfeit currency detection. The analysis has been performed using, paired sample t-test and elementary time series forecasting in IBM SPSS 21 and RStudio respectively. Data points from 1999-00 to 2017-18 have been collected from Reserve Bank of India Annual reports under currency management section.
Index Terms: Counterfeiting, Banknotes, Data forgery, Currency, Paired Sample t-test, Holt-Winters, Time Series Forecasting, Currency Management, Reserve Bank of India, Security Features, Outliers, Normality.

Scope of the Article: Data Management