scholarly journals Can innovative trend analysis identify trend change points?

2020 ◽  
Vol 1 (3) ◽  
pp. 6-15
Author(s):  
Sadık Alashan
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Statistical Significance ◽  
Bootstrap Method ◽  
Change Points ◽  
Trend Detection ◽  
Significance Level ◽  
Related Data ◽  
Innovative Trend Analysis ◽  
June July

Trends in temperature series are the main cause of climate change. Because solar energy directs hydro-meteorological events and increasing variations in this resource change the balance between events such as evaporation, wind, and rainfall. There are many methods for calculating trends in a time series such as Mann-Kendall, Sen's slope estimator, Spearman's rho, linear regression and the new Sen innovative trend analysis (ITA). In addition, Mann-Kendall's variant, the sequential Mann Kendall, has been developed to identify trend change points; however, it is sensitive to related data as specified by some researchers. Şen_ITA is a new trend detection method and does not require independent and normally distributed time series, but has never been used to detect trend change points. In the literature, multiple, half-time and multi-durations ITA methods are used to calculate partial trends in a time series without identifying trend change points. In this study, trend change points are detected using the Şen_ITA method and named ITA_TCP. This approach may allow researchers to identify trend change points in a time series. Diyarbakır (Turkey) is selected as a study area, and ITA_TCP has detected trends and trends change points in monthly average temperatures. Although ITA detects only a significant upward trend in August, given the 95% statistical significance level, ITA_TCP shows three upward trends in June, July and August, and a decreasing trend in September. Critical trend slope values are obtained using the bootstrap method, which does not require the normal distribution assumption.

scholarly journals Can innovative trend analysis identify trend change points?

2020 ◽  
Vol 1 (3) ◽  
pp. 6-15
Author(s):  
Sadık Alashan
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Statistical Significance ◽  
Bootstrap Method ◽  
Change Points ◽  
Trend Detection ◽  
Significance Level ◽  
Related Data ◽  
Innovative Trend Analysis ◽  
June July

Trends in temperature series are the main cause of climate change. Because solar energy directs hydro-meteorological events and increasing variations in this resource change the balance between events such as evaporation, wind, and rainfall. There are many methods for calculating trends in a time series such as Mann-Kendall, Sen's slope estimator, Spearman's rho, linear regression and the new Sen innovative trend analysis (ITA). In addition, Mann-Kendall's variant, the sequential Mann Kendall, has been developed to identify trend change points; however, it is sensitive to related data as specified by some researchers. Şen_ITA is a new trend detection method and does not require independent and normally distributed time series, but has never been used to detect trend change points. In the literature, multiple, half-time and multi-durations ITA methods are used to calculate partial trends in a time series without identifying trend change points. In this study, trend change points are detected using the Şen_ITA method and named ITA_TCP. This approach may allow researchers to identify trend change points in a time series. Diyarbakır (Turkey) is selected as a study area, and ITA_TCP has detected trends and trends change points in monthly average temperatures. Although ITA detects only a significant upward trend in August, given the 95% statistical significance level, ITA_TCP shows three upward trends in June, July and August, and a decreasing trend in September. Critical trend slope values are obtained using the bootstrap method, which does not require the normal distribution assumption.

scholarly journals Effects of the prewhitening method, the time granularity, and the time segmentation on the Mann–Kendall trend detection and the associated Sen's slope

2020 ◽  
Vol 13 (12) ◽  
pp. 6945-6964
Author(s):  
Martine Collaud Coen ◽  
Elisabeth Andrews ◽  
Alessandro Bigi ◽  
Giovanni Martucci ◽  
Gonzague Romanens ◽  
...  
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Statistical Significance ◽  
Parametric Method ◽  
Kendall Test ◽  
Trend Detection ◽  
Temporal Segmentation ◽  
Sen’S Slope ◽  
Power Of The Test ◽  
Original Time

Abstract. The Mann–Kendall test associated with the Sen's slope is a very widely used non-parametric method for trend analysis. It requires serially uncorrelated time series, yet most of the atmospheric processes exhibit positive autocorrelation. Several prewhitening methods have therefore been designed to overcome the presence of lag-1 autocorrelation. These include a prewhitening, a detrending and/or a correction of the detrended slope and the original variance of the time series. The choice of which prewhitening method and temporal segmentation to apply has consequences for the statistical significance, the value of the slope and of the confidence limits. Here, the effects of various prewhitening methods are analyzed for seven time series comprising in situ aerosol measurements (scattering coefficient, absorption coefficient, number concentration and aerosol optical depth), Raman lidar water vapor mixing ratio, as well as tropopause and zero-degree temperature levels measured by radio-sounding. These time series are characterized by a broad variety of distributions, ranges and lag-1 autocorrelation values and vary in length between 10 and 60 years. A common way to work around the autocorrelation problem is to decrease it by averaging the data over longer time intervals than in the original time series. Thus, the second focus of this study evaluates the effect of time granularity on long-term trend analysis. Finally, a new algorithm involving three prewhitening methods is proposed in order to maximize the power of the test, to minimize the number of erroneous detected trends in the absence of a real trend and to ensure the best slope estimate for the considered length of the time series.

scholarly journals Effects of the prewhitening method, the time granularity and the time segmentation on the Mann-Kendall trend detection and the associated Sen's slope

2020 ◽  
Author(s):  
Martine Collaud Coen ◽  
Elisabeth Andrews ◽  
Alesssandro Bigi ◽  
Gonzague Romanens ◽  
Giovanni Martucci ◽  
...  
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Statistical Significance ◽  
Parametric Method ◽  
Kendall Test ◽  
Trend Detection ◽  
Temporal Segmentation ◽  
Sen’S Slope ◽  
Power Of The Test ◽  
Mann Kendall Test

Abstract. The most widely used non-parametric method for trend analysis is the Mann-Kendall test associated with the Sen's slope. The Mann-Kendall test requires serially uncorrelated time series, whereas most of the atmospheric processes exhibit positive autocorrelation. Several prewhitening methods have been designed to overcome the presence of lag-1 autocorrelation. These include a prewhitening, a detrending and/or a correction for the detrended slope and the original variance of the time series. The choice of which prewhitening method and temporal segmentation to apply has consequences for the statistical significance, the value of the slope and of the confidence limits. Here, the effects of various prewhitening methods are analyzed for seven time series comprising in-situ aerosol measurements (scattering coefficient, absorption coefficient, number concentration and aerosol optical depth), Raman Lidar water vapor mixing ratio and the tropopause and zero degree levels measured by radio-sounding. These time series are characterized by a broad variety of distributions, ranges and lag-1 autocorrelation values and vary in length between 10 and 60 years. A common way to work around the autocorrelation problem is to decrease it by averaging the data over longer time intervals than in the original time series. Thus, the second focus of this study is evaluation of the effect of time granularity on long-term trend analysis. Finally, a new algorithm involving three prewhitening methods is proposed in order to maximize the power of the test, to minimize the amount of erroneous detected trends in the absence of a real trend and to ensure the best slope estimate for the considered length of the time series.

scholarly journals Trend Analysis of Annual and Seasonal River Runoff by Using Innovative Trend Analysis with Significant Test

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 95
Author(s):  
Yilinuer Alifujiang ◽  
Jilili Abuduwaili ◽  
Yongxiao Ge
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
River Runoff ◽  
Temporal Trends ◽  
Temporal Distribution ◽  
Trend Test ◽  
Data Series ◽  
Mk Test ◽  
Comparison Results ◽  
Innovative Trend Analysis

This study investigated the temporal patterns of annual and seasonal river runoff data at 13 hydrological stations in the Lake Issyk-Kul basin, Central Asia. The temporal trends were analyzed using the innovative trend analysis (ITA) method with significance testing. The ITA method results were compared with the Mann-Kendall (MK) trend test at a 95% confidence level. The comparison results revealed that the ITA method could effectively identify the trends detected by the MK trend test. Specifically, the MK test found that the time series percentage decreased from 46.15% in the north to 25.64% in the south, while the ITA method revealed a similar rate of decrease, from 39.2% to 29.4%. According to the temporal distribution of the MK test, significantly increasing (decreasing) trends were observed in 5 (0), 6 (2), 4 (3), 8 (0), and 8 (1) time series in annual, spring, summer, autumn, and winter river runoff data. At the same time, the ITA method detected significant trends in 7 (1), 9 (3), 6(3), 9 (3), and 8 (2) time series in the study area. As for the ITA method, the “peak” values of 24 time series (26.97%) exhibited increasing patterns, 25 time series (28.09%) displayed increasing patterns for “low” values, and 40 time series (44.94%) showed increasing patterns for “medium” values. According to the “low”, “medium”, and “peak” values, five time series (33.33%), seven time series (46.67%), and three time series (20%) manifested decreasing trends, respectively. These results detailed the patterns of annual and seasonal river runoff data series by evaluating “low”, “medium”, and “peak” values.

scholarly journals Using the Bootstrap Method for a Statistical Significance Test of Differences between Summary Histograms

2006 ◽  
Vol 134 (5) ◽  
pp. 1442-1453 ◽  
Author(s):  
Kuan-Man Xu
Keyword(s):  
Euclidean Distance ◽  
Probability Distributions ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Bootstrap Method ◽  
Statistical Hypothesis ◽  
Object Size ◽  
Size Category ◽  
Significance Level ◽  
The Bootstrap Method

Abstract A new method is proposed to compare statistical differences between summary histograms, which are the histograms summed over a large ensemble of individual histograms. It consists of choosing a distance statistic for measuring the difference between summary histograms and using a bootstrap procedure to calculate the statistical significance level. Bootstrapping is an approach to statistical inference that makes few assumptions about the underlying probability distribution that describes the data. Three distance statistics are compared in this study. They are the Euclidean distance, the Jeffries–Matusita distance, and the Kuiper distance. The data used in testing the bootstrap method are satellite measurements of cloud systems called “cloud objects.” Each cloud object is defined as a contiguous region/patch composed of individual footprints or fields of view. A histogram of measured values over footprints is generated for each parameter of each cloud object, and then summary histograms are accumulated over all individual histograms in a given cloud-object size category. The results of statistical hypothesis tests using all three distances as test statistics are generally similar, indicating the validity of the proposed method. The Euclidean distance is determined to be most suitable after comparing the statistical tests of several parameters with distinct probability distributions among three cloud-object size categories. Impacts on the statistical significance levels resulting from differences in the total lengths of satellite footprint data between two size categories are also discussed.

scholarly journals Power of parametric and non-parametric tests for trend detection in annual maximum series

2019 ◽  
Author(s):  
Vincenzo Totaro ◽  
Andrea Gioia ◽  
Vito Iacobellis
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Probability Distribution ◽  
Generalized Extreme Value Distribution ◽  
Change Points ◽  
Trend Detection ◽  
Correct Selection ◽  
Stationary Probability ◽  
Parametric Methods ◽  
Non Parametric

Abstract. The need of fitting time series characterized by the presence of trend or change points has generated in latest years an increased interest in the investigation of non-stationary probability distributions. Considering that the available hydrological time series can be recognized as the observable part of a stochastic process with a definite probability distribution, two main topics can be tackled in this context: the first one is related to the definition of an objective criterion for choosing whether the stationary hypothesis can be adopted, while the second one regards the effects of non-stationarity on the estimation of distribution parameters and quantiles for assigned return period and flood risk evaluation. Although the time series trend or change points can be recognized using classical tests available in literature (e.g. Mann–Kendal or CUSUM test), for design purpose it is still required the correct selection of the stationary or non-stationary probability distribution. By this light, the focus is shifted toward model selection criteria which implies the use of parametric methods with all related issues on parameters estimation. The aim of this study is to compare the performance of parametric and non-parametric methods for trend detection analysing their power and focusing on the use of traditional model selection tools (e.g. Akaike Information Criterion and Likelihood Ratio test) within this context. Power and efficiency of parameter estimation, including the trend coefficient, were investigated through Monte Carlo simulations using Generalized Extreme Value distribution as parent with selected parameter sets.

Machine Intelligence-Based Trend Analysis of COVID-19 for Total Daily Confirmed Cases in Asia and Africa

2021 ◽  
pp. 164-185
Author(s):  
Yibeltal Meslie ◽  
Wegayehu Enbeyle ◽  
Binay Kumar Pandey ◽  
Sabyasachi Pramanik ◽  
Digvijay Pandey ◽  
...  
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Moving Average ◽  
Time Frame ◽  
Machine Intelligence ◽  
Public Health Services ◽  
Average Order ◽  
Significance Level ◽  
The First Variation ◽  
Health Organization

COVID-19 is likely to pose a significant threat to healthcare, especially for disadvantaged populations due to the inadequate condition of public health services with people's lack of financial ways to obtain healthcare. The primary intention of such research was to investigate trend analysis for total daily confirmed cases with new corona virus (i.e., COVID-19) in the countries of Africa and Asia. The study utilized the daily recorded time series observed for two weeks (52 observations) in which the data is obtained from the world health organization (WHO) and world meter website. Univariate ARIMA models were employed. STATA 14.2 and Minitab 14 statistical software were used for the analysis at 5% significance level for testing hypothesis. Throughout time frame studied, because all four series are non-stationary at level, they became static after the first variation. The result revealed the appropriate time series model (ARIMA) for Ethiopia, Pakistan, India, and Nigeria were Moving Average order 2, ARIMA(1, 1, 1), ARIMA(2, 1, 1), and ARIMA (1, 1, 2), respectively.

scholarly journals Finnish National Phenological Network 1997–2017: from observations to trend detection

2020 ◽  
Vol 64 (10) ◽  
pp. 1783-1793
Author(s):  
Samuli Helama ◽  
Anne Tolvanen ◽  
Jouni Karhu ◽  
Jarmo Poikolainen ◽  
Eero Kubin
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Temporal Trends ◽  
Early Summer ◽  
Trend Detection ◽  
Phenological Stages ◽  
Study Region ◽  
Single Tree ◽  
Multiple Observations ◽  
Multiple Trees

Abstract Plant phenological dataset collected at 42 sites across the mainland of Finland and covering the years 1997–2017 is presented and analysed for temporal trends. The dataset of n = 16,257 observations represents eleven plant species and fifteen phenological stages and results in forty different variables, i.e. phenophases. Trend analysis was carried out for n = 808 phenological time-series that contained at least 10 observations over the 21-year study period. A clear signal of advancing spring and early-summer phenology was detected, 3.4 days decade−1, demonstrated by a high proportion of negative trends for phenophases occurring in April through June. Latitudinal correlation indicated stronger signal of spring and early-summer phenology towards the northern part of the study region. The autumn signal was less consistent and showed larger within-site variations than those observed in other seasons. More than 60% of the dates based on single tree/monitoring square were exactly the same as the averages from multiple trees/monitoring squares within the site. In particular, the reliability of data on autumn phenology was increased by multiple observations per site. The network is no longer active.

scholarly journals Detection of abrupt shift and non-parametric analyses of trends in runoff time series in Dez River Basin

2021 ◽  
Author(s):  
Hooman Gholami ◽  
Yahya Moradi ◽  
Morteza Lotfirad ◽  
Mohammad Amin Gandomi ◽  
Nooredin Bazgir ◽  
...  
Keyword(s):  
Time Series ◽  
Change Point ◽  
Change Points ◽  
Limit Values ◽  
June July August ◽  
Level Of Confidence ◽  
Observatory Data ◽  
Parametric Analyses ◽  
June July ◽  
Hydrometric Station

Abstract The present study aims to investigate the homogeneity of runoff time series and also to review the existence of trends in Tale Zang hydrometric station (the hydrometric station in the entrance of Dez Reservoir) runoff, using 61 years (1956–2016) daily observatory data. Pettit test, which is a common method in investigating the homogeneity of time series, was used to identify change points. Both Mann-Kendall and auto-correlated Mann-Kendall tests were applied to analyze the existence or non-existence of trends in each annual, seasonal, and monthly time series observed in a runoff. In time series, significant trends in 95% level of confidence were recognized, upper and lower limit values were presented for Sen's slope and it was tested for the increasing or decreasing trends in nature. Based on the results of this study, the significant change point in 95% level of confidence was recognized in Annual, Spring, Summer, Autumn, March, May, June, July, August, September, and October in 1997,1997, 1999, 1997, 1999, 1999, 1999, 1997, 2000, 2007, 2008, respectively. Analyzing the existence of a trend in 95% level of confidence indicated that in Spring, and in March, September, and October, for both Mann-Kendall and auto-correlated Mann-Kendall tests, the trend is significant and additive until the change point.

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