Estimating the Similarities of G7 Countries Using Economic Parameters

Hira, Swati; Bai, Anita; Deshpande, Parag S.

doi:10.1007/978-81-322-2757-1_7

Cited by 1 publication

(2 citation statements)

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“…So, we decided to use recurrence based methods RR, CRP, and CPR to identify the differences in climate on the basis of temperature for nine US and eight Chinese regions and also the probabilistic correlations between time series. The accuracy of the recurrence based approach is validated on real and synthetic datasets and analyzed using ANOVA, Kruskal-Wallis, and -statistical significance tests [27,28].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This should be done using a statistical test and an appropriate null hypothesis significance test. So, for the statistical significance of our approach, we are using ANOVA, Kruskal-Wallis, and -significance test [27,28] against the analysis obtained from the recurrence based method on time series data. The statistical methods can be used to analyze similarity between two distinct pieces of data.…”

Section: Validation Using Significancementioning

confidence: 99%

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Recurrence Based Similarity Identification of Climate Data

Bai

Hira²,

Parag

2017

Discrete Dynamics in Nature and Society

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Climate change has become a challenging and emerging research problem in many research related areas. One of the key parameters in analyzing climate change is to analyze temperature variations in different regions. The temperature variation in a region is periodic within the interval. Temperature variations, though periodic in nature, may vary from one region to another and such variations are mainly dependent on the location and altitude of the region and also on other factors like the nearness of sea and vegetation.In this paper, we analyze such periodic variations using recurrence plot (RP), cross recurrence plot (CRP), recurrence rate (RR), and correlation of probability of recurrence (CPR) methods to find similarities of periodic variations between and within climatic regions and to identify their connectivity trend. First, we test the correctness of our method by applying it on voice and heart rate data and then experimentation is performed on synthetic climate data of nine regions in the United States and eight regions in China. Finally, the accuracy of our approach is validated on both real and synthetic datasets and demonstrated using ANOVA, Kruskal-Wallis, and z-statistics significance tests.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Validation Using Significancementioning

confidence: 99%