Assessment of the Running Slope Difference (RSD) t-Test, a new statistical method for detecting climate trend turning

2020 ◽  
Author(s):  
Bin Zuo ◽  
Zhaolu Hou ◽  
Fei Zheng ◽  
Lifang Sheng ◽  
Yang Gao ◽  
...  
Keyword(s):  
Time Series ◽  
Turning Points ◽  
Surface Air Temperature ◽  
T Test ◽  
Test Method ◽  
Detection Methods ◽  
Climate Trend ◽  
Warming Rate ◽  
Slope Difference ◽  
Climate Time Series

<p>Global mean surface air temperature (GMT) rose roughly 0.85 °C from 1880 to 2012 (IPCC 2013), attributing mainly to an increase in atmospheric greenhouse gases. For different decadal timescale periods in the past 100 years, the warming rate of different periods may significantly different. For example, IPCC AR1 (1990) point out that GMT between 1910-1940 and 1975-1990 are significantly warming, meanwhile GMT stay nearly constant between 1940 and early 1970. The phenomenon of two nearby periods showing significantly different trends is knowing as trend turning, this phenomenon is common in climate time series and crucial when climate change is investigated. However, the available detection methods for climate trend turnings are relatively few, especially for the methods which have the ability of detecting multiple trend turnings. We propose a new methodology named as the running slope difference (RSD) t-test to detect multiple trend turnings. This method employs a t-distributed statistic of slope difference to test the sub-series trends difference of the time series, thereby identify the turning-points. We compare the RSD t-test method with some other existing trend turning detection methods with an idealized time series case and several climate time series cases. And we also report the Monte Carlo simulation used to evaluate this method’s detection ability. Results show that the RSD t-test method is an effective tool for detecting trend turning in time series, and this method has three major advantages: ability to detect multiple turning-points, capacity to detect all three types of trend turning, and great performance of avoiding false alarm.</p>

scholarly journals A Mathematical Modeling Framework To Detect The Optimal Financial Turning Points

2021 ◽  
Author(s):  
Fateme Yazdani ◽  
Mehdi Khashei ◽  
Seyed Reza Hejazi
Keyword(s):  
Mathematical Modeling ◽  
Time Series ◽  
Financial Time Series ◽  
Turning Points ◽  
Real Data ◽  
Detection Methods ◽  
Modeling Framework ◽  
Detection Model ◽  
Financial Time ◽  
History Of

Abstract The financial markets have always witnessed the competition of their participants for gaining high and stable profits. The realization extent of this goal depends on the profitability of the trading points or turning points (TPs) ahead. TPs prediction problem is one of the most challenging yet important problems in the financial discipline. The first step towards predicting financial TPs is to detect TPs from the history of the corresponding financial time series. Literature indicates that the profitability of the predicted financial TPs depends on the profitability of the detected TPs. Given this, numerous efforts have been devoted to enhancing the profitability of the detected financial TPs. Nevertheless, to the best of our knowledge, none of the existing detection methods can detect the most profitable or the optimal TPs from the history of financial time series. The present study concerns this research gap and ensures detecting the optimal financial TPs by proposing a mathematical modeling framework. The proposed optimal TPs detection model in this paper will be structured concerning the three following assumptions. First, short-selling the financial asset is possible. Second, the time value for the investment money is not considered. Third, detecting consecutive buying TPs and consecutive selling TPs is not allowed. Empirical results with twenty real data sets indicate that the proposed model, in contrast to the existing TPs detection methods, detects the optimal TPs from the history of the financial time series.

MODEL MANAJEMEN PENDIDIKAN KARAKTER BERBASIS INTEGRASI NILAI-NILAI RELIGIUS DAN MULTIKULTURAL

2019 ◽  
Vol 20 (2) ◽  
pp. 227-266
Author(s):  
Rohmat Rohmat
Keyword(s):  
Character Education ◽  
Future Generation ◽  
T Test ◽  
Test Method ◽  
Intellectual Ability ◽  
Management Model ◽  
Education Management ◽  
Model Based ◽  
Before And After ◽  
Emotional Aspects

Securing religious and multicultural character values ​​is very important to be instilled as early as possible through education. This is due to education not only providing increased intellectual ability, but also is responsible for integrating character values ​​in students. At least there are some aspects that need to be developed in the education phase of school children including intellectual aspects, emotional aspects, social aspects, physical aspects, aspects of movement, aesthetic aspects, and moral aspects. Based on this opinion it can be seen that the cultivation of religious and multicultural character values ​​in madrasa ibtidaiyah level students needs to be done in order to realize a future generation of adults and character. On the other hand, the cultivation of multicultural values ​​is also an urgent matter to do.This study aims to find a character education management model based on the integration of religious and multicultural values ​​in MI Banyumas Regency. The research method with research and development methods. The results of the study of the character education management model based on the integration of religious and multicultural values ​​that were developed effectively for use in character education in Madrasah Ibtidaiyah. The results of the t test through the paired t test method showed that there were significant differences between the character values ​​before and after the implementation of character education in Madrasah Ibtidaiyah. These results reinforce that character education is effectively used in Madrasah Ibtidaiyah.

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

scholarly journals Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hitoshi Iuchi ◽  
Michiaki Hamada
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Time Course ◽  
Time Series Data ◽  
Expression Patterns ◽  
Detection Methods ◽  
Series Data ◽  
Expression Levels ◽  
Over Time ◽  
Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

Econometrics for Modelling Climate Change

2021 ◽  
Author(s):  
Jennifer L. Castle ◽  
David F. Hendry
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Econometric Modeling ◽  
Complex Data ◽  
Explanatory Variables ◽  
Stochastic Trends ◽  
Trend Breaks ◽  
Abrupt Shifts ◽  
Climate Time Series ◽  
Do So

Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.

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