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pyMannKendall: a python package for non parametric Mann Kendall family of trend tests.

pyMannKendall: حزمة python لعائلة Mann Kendall غير البارامترية من اختبارات الاتجاه.
Authors: Md. Manjurul Hussain; Ishtiak Mahmud;

pyMannKendall: a python package for non parametric Mann Kendall family of trend tests.

Abstract

L'analyse des tendances est l'une des mesures les plus importantes dans l'étude des données des séries chronologiques. Les tests paramétriques et non paramétriques sont couramment utilisés dans l'analyse des tendances. Les tests paramétriques exigent que les données soient indépendantes et normalement distribuées. D'autre part, les tests de tendance non paramétriques exigent seulement que les données soient indépendantes et puissent tolérer des valeurs aberrantes dans les données (Hamed & Rao, 1998). Cependant, les tests paramétriques sont plus puissants que les tests non paramétriques.

El análisis de tendencias es una de las mediciones más importantes en el estudio de los datos de series de tiempo. Tanto las pruebas paramétricas como las no paramétricas se utilizan comúnmente en el análisis de tendencias. Las pruebas paramétricas requieren que los datos sean independientes y se distribuyan normalmente. Por otro lado, las pruebas de tendencias no paramétricas solo requieren que los datos sean independientes y puedan tolerar valores atípicos en los datos (Hamed y Rao, 1998). Sin embargo, las pruebas paramétricas son más poderosas que las no paramétricas.

Trend analysis is one of the most important measurements in studying time series data.Both parametric and non-parametric tests are commonly used in trend analysis.Parametric tests require data to be independent and normally distributed.On the other hand, non-parametric trend tests require only that the data be independent and can tolerate outliers in the data (Hamed & Rao, 1998).However, parametric tests are more powerful than nonparametric ones.

يعد تحليل الاتجاه أحد أهم القياسات في دراسة بيانات السلاسل الزمنية. يستخدم كل من الاختبارات البارامترية وغير البارامترية بشكل شائع في تحليل الاتجاه. تتطلب الاختبارات البارامترية أن تكون البيانات مستقلة وموزعة بشكل طبيعي. من ناحية أخرى، تتطلب اختبارات الاتجاه غير البارامترية فقط أن تكون البيانات مستقلة ويمكن أن تتسامح مع القيم المتطرفة في البيانات (حامد وراو، 1998). ومع ذلك، فإن الاختبارات البارامترية أقوى من الاختبارات غير البارامترية.

Keywords

Global and Planetary Change, Atmospheric Science, Python (programming language), Climate Change and Variability Research, Numerical Weather Prediction Models, Computer science, Programming language, Earth and Planetary Sciences, Environmental Science, Physical Sciences, Global Drought Monitoring and Assessment, FOS: Mathematics, Probabilistic Forecasting, Mathematics

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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