Review of trend detection methods and their application to detect temperature changes in India

2013 ◽  
Vol 476 ◽  
pp. 212-227 ◽  
Author(s):  
P. Sonali ◽  
D. Nagesh Kumar
Keyword(s):  
Trend Detection ◽  
Detection Methods ◽  
Temperature Changes

Precipitation and temperature changes in eastern India by multiple trend detection methods

2016 ◽  
Vol 180 ◽  
pp. 211-225 ◽  
Author(s):  
Chandra Shekhar Sharma ◽  
Sudhindra N. Panda ◽  
Rudra P. Pradhan ◽  
Amanpreet Singh ◽  
Akira Kawamura
Keyword(s):  
Trend Detection ◽  
Detection Methods ◽  
Eastern India ◽  
Temperature Changes ◽  
Precipitation And Temperature

scholarly journals Trend detection of atmospheric time series

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Kai-Lan Chang ◽  
Martin G. Schultz ◽  
Xin Lan ◽  
Audra McClure-Begley ◽  
Irina Petropavlovskikh ◽  
...  
Keyword(s):  
Time Series ◽  
Atmospheric Chemistry ◽  
Surface Ozone ◽  
High Surface ◽  
Effective Means ◽  
Environmental Data ◽  
Model Specification ◽  
Trend Detection ◽  
Detection Methods ◽  
Sources Of Uncertainty

This paper is aimed at atmospheric scientists without formal training in statistical theory. Its goal is to (1) provide a critical review of the rationale for trend analysis of the time series typically encountered in the field of atmospheric chemistry, (2) describe a range of trend-detection methods, and (3) demonstrate effective means of conveying the results to a general audience. Trend detections in atmospheric chemical composition data are often challenged by a variety of sources of uncertainty, which often behave differently to other environmental phenomena such as temperature, precipitation rate, or stream flow, and may require specific methods depending on the science questions to be addressed. Some sources of uncertainty can be explicitly included in the model specification, such as autocorrelation and seasonality, but some inherent uncertainties are difficult to quantify, such as data heterogeneity and measurement uncertainty due to the combined effect of short and long term natural variability, instrumental stability, and aggregation of data from sparse sampling frequency. Failure to account for these uncertainties might result in an inappropriate inference of the trends and their estimation errors. On the other hand, the variation in extreme events might be interesting for different scientific questions, for example, the frequency of extremely high surface ozone events and their relevance to human health. In this study we aim to (1) review trend detection methods for addressing different levels of data complexity in different chemical species, (2) demonstrate that the incorporation of scientifically interpretable covariates can outperform pure numerical curve fitting techniques in terms of uncertainty reduction and improved predictability, (3) illustrate the study of trends based on extreme quantiles that can provide insight beyond standard mean or median based trend estimates, and (4) present an advanced method of quantifying regional trends based on the inter-site correlations of multisite data. All demonstrations are based on time series of observed trace gases relevant to atmospheric chemistry, but the methods can be applied to other environmental data sets.

scholarly journals A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

2021 ◽  
Vol 11 (21) ◽  
pp. 10403
Author(s):  
Corbinian Nentwich ◽  
Gunther Reinhart
Keyword(s):  
Anomaly Detection ◽  
Condition Monitoring ◽  
Production Systems ◽  
Detection System ◽  
Industrial Robot ◽  
Health Indicators ◽  
Automated System ◽  
Trend Detection ◽  
Detection Methods ◽  
Application Data

Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this publication, such a system is presented and suitable anomaly detection and trend detection methods for the system are selected based on synthetic and real world industrial application data. A statistical test, namely the Cox-Stuart test, appears to be the most suitable approach for trend detection and the local outlier factor algorithm or the long short-term neural network performs best for anomaly detection in the application of industrial robot gear condition monitoring in the presented experiments.

scholarly journals Nonlinear Trends, Long-Range Dependence, and Climate Noise Properties of Surface Temperature

2012 ◽  
Vol 25 (12) ◽  
pp. 4172-4183 ◽  
Author(s):  
Christian Franzke
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Null Model ◽  
Warming Trend ◽  
Temperature Time Series ◽  
Trend Detection ◽  
Detection Methods ◽  
Nonlinear Trends ◽  
Temperature Time ◽  
Climate Noise

Abstract This study investigates the significance of trends of four temperature time series—Central England Temperature (CET), Stockholm, Faraday-Vernadsky, and Alert. First the robustness and accuracy of various trend detection methods are examined: ordinary least squares, robust and generalized linear model regression, Ensemble Empirical Mode Decomposition (EEMD), and wavelets. It is found in tests with surrogate data that these trend detection methods are robust for nonlinear trends, superposed autocorrelated fluctuations, and non-Gaussian fluctuations. An analysis of the four temperature time series reveals evidence of long-range dependence (LRD) and nonlinear warming trends. The significance of these trends is tested against climate noise. Three different methods are used to generate climate noise: (i) a short-range-dependent autoregressive process of first order [AR(1)], (ii) an LRD model, and (iii) phase scrambling. It is found that the ability to distinguish the observed warming trend from stochastic trends depends on the model representing the background climate variability. Strong evidence is found of a significant warming trend at Faraday-Vernadsky that cannot be explained by any of the three null models. The authors find moderate evidence of warming trends for the Stockholm and CET time series that are significant against AR(1) and phase scrambling but not the LRD model. This suggests that the degree of significance of climate trends depends on the null model used to represent intrinsic climate variability. This study highlights that in statistical trend tests, more than just one simple null model of intrinsic climate variability should be used. This allows one to better gauge the degree of confidence to have in the significance of trends.

scholarly journals Analyzing Trend and Variability of Rainfall in The Tafna Basin (Northwestern Algeria)

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 347 ◽  
Author(s):  
Hanane Bougara ◽  
Kamila Baba Hamed ◽  
Christian Borgemeister ◽  
Bernhard Tischbein ◽  
Navneet Kumar
Keyword(s):  
Time Series ◽  
Water Resource Management ◽  
Statistical Tests ◽  
Trend Detection ◽  
Detection Methods ◽  
Annual Rainfall ◽  
Rainfall Time Series ◽  
Friedman Test ◽  
Upward Trend ◽  
Increasing Trend

Northwest Algeria has experienced fluctuations in rainfall between the two decades 1940s and 1990s from positive to negative anomalies, which reflected a significant decline in rainfall during the mid-1970s. Therefore, further analyzing rainfall in this region is required for improving the strategies on water resource management. In this study, we complement previous studies by dealing with sub basins that were not previously addressed in Tafna basin (our study area located in Northwest Algeria), and by including additional statistical methods (Kruskal–Wallis test, Jonckheere-Terpstra test, and the Friedman test) that were not earlier reported on the large scale (Northwest Algeria). In order to analyse the homogeneity, trends, and stationarity in rainfall time series for nine rainfall stations over the period 1979–2011, we have used several statistical tests. The results showed an increasing trend for annual rainfall after the break detected in 2007 for Djbel Chouachi, Ouled Mimoun, Sidi Benkhala stations using Hubert, Pettitt, and Buishand tests. The Lee and Heghinian test has detected a break at the same year in 2007 for all stations except Sebdou, Beni Bahdel, and Hennaya stations, which have a break date in 1980. We have confirmed this increasing trend for rainfall with other trend detection methods such as Mann Kendall and Sen’s method that highlighted an upward trend for all the stations in the autumn season, which is mainly due to an increase in rainfall in September and October. On a monthly scale, the date of rupture is different from one station to another because the time series are not homogeneous. In addition, we have applied three tests enabling further results: (i) the Jonckheere-Terpstra test has detected an upward trend for two stations (Khemis and Hennaya), (ii) Friedman test has indicated the difference between the mean rank again with Khemis and Hennaya stations and the Merbeh station, (iii) according to the Kruskal-Wallis test, there have been no variance detected between all the rainfall stations. The increasing trend in rainfall may lead to a rise in stream flow and enhance potential floods risks in low-lying regions of the study area.

scholarly journals Analysis of the Recent Trends of Two Climate Parameters over Two Eco-Regions of Ethiopia

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 161 ◽  
Author(s):  
Mohammed Gedefaw ◽  
Denghua Yan ◽  
Hao Wang ◽  
Tianling Qin ◽  
Kun Wang
Keyword(s):  
Meteorological Data ◽  
Decision Makers ◽  
Trend Detection ◽  
Bahir Dar ◽  
Temperature Changes ◽  
Temperature Trends ◽  
Increasing Trend ◽  
Recent Trends ◽  
Slope Estimator ◽  
Precipitation And Temperature

The changes in climatic variables in Ethiopia are not entirely understood. This paper investigated the recent trends of precipitation and temperature on two eco-regions of Ethiopia. This study used the observed historical meteorological data from 1980 to 2016 to analyze the trends. Trend detection was done by using the non-parametric Mann-Kendall (MK), Sen’s slope estimator test, and Innovative Trend Analysis Method (ITAM). The results showed that a significant increasing trend was observed in the Gondar, Bahir Dar, Gewane, Dembi-Dolo, and Negele stations. However, a slightly decreasing trend was observed in the Sekoru, Degahabur, and Maichew stations regarding precipitation trends. As far as the trend of temperature was concerned, an increasing trend was detected in the Gondar, Bahir Dar, Gewane, Degahabur, Negele, Dembi-Dolo, and Maichew stations. However, the temperature trend in Sekoru station showed a sharp decreasing trend. The effects of precipitation and temperature changes on water resources are significant after 1998. The consistency in the precipitation and temperature trends over the two eco-regions confirms the robustness of the changes. The findings of this study will serve as a reference for climate researchers, policy and decision makers.

scholarly journals Spatiotemporal Dynamics of Precipitation in Southwest Arid-Agriculture Zones of Pakistan

2020 ◽  
Vol 12 (6) ◽  
pp. 2305 ◽  
Author(s):  
Muhammad Waseem ◽  
Ijaz Ahmad ◽  
Ahmad Mujtaba ◽  
Muhammad Tayyab ◽  
Chen Si ◽  
...  
Keyword(s):  
Climate Change ◽  
Kendall Test ◽  
Trend Detection ◽  
Detection Methods ◽  
Precipitation Pattern ◽  
Study Region ◽  
Variability Analysis ◽  
Variability Index ◽  
Seasonal Analysis ◽  
Arid Agriculture

Investigation of spatiotemporal precipitation trends from a climate change perspective is essential, especially in those regions with rainfed agriculture in order to propose sustainable adaptation schemes. Some restrictive assumptions may hinder the efficacy of trend detection methods, so it could be supported with variability analysis to have a clear picture of the spatiotemporal precipitation dynamics rather than focusing on a single approach. Hence, in the current study, a spatiotemporal dynamic analysis of precipitation was carried out using trend detection methods (the innovative trend analysis method and Mann–Kendall test) and statistical indices (the consecutive disparity index, entropy-based variability index and absolute inter-variability index) in the southwest arid region of Pakistan. The results indicated that based on the monthly, annual and seasonal time series, no systematic precipitation pattern was observed across the whole study region. However, on average, an increasing trend was observed in the east plateau while decreasing in the west plateau. The variability analysis also signposted the higher variability in the case of the western plateau and coastal area compared to the east plateau. Based on the seasonal analysis, it was concluded that, on average, precipitation in the winter and spring season goes on decreasing with higher variability while a mixture of increasing and decreasing trends resulted for summer and autumn. Conclusively the study found that precipitation in the study area is more erratic and its behaviour abruptly changed over a short distance. Moreover, discrepancies and inconstancies were found in the selected trend detection approaches and variability indices. The results also indicated that climate change is going to seriously affect the region as a decreasing trend prevails in most of the cases and stations.

A simulation study of trend detection methods for integrated ecosystem assessment

2019 ◽  
Vol 76 (7) ◽  
pp. 2060-2069 ◽  
Author(s):  
Sean Hardison ◽  
Charles T Perretti ◽  
Geret S DePiper ◽  
Andrew Beet
Keyword(s):  
Time Series ◽  
Generalized Least Squares ◽  
Kendall Test ◽  
Trend Detection ◽  
Detection Methods ◽  
Times Series ◽  
Model Selection Approach ◽  
Mann Kendall Test ◽  
Ecosystem Level ◽  
Rejection Rates

Abstract The identification of trends in ecosystem indicators has become a core component of ecosystem approaches to resource management, although oftentimes assumptions of statistical models are not properly accounted for in the reporting process. To explore the limitations of trend analysis of short times series, we applied three common methods of trend detection, including a generalized least squares model selection approach, the Mann–Kendall test, and Mann–Kendall test with trend-free pre-whitening to simulated time series of varying trend and autocorrelation strengths. Our results suggest that the ability to detect trends in time series is hampered by the influence of autocorrelated residuals in short series lengths. While it is known that tests designed to account for autocorrelation will approach nominal rejection rates as series lengths increase, the results of this study indicate biased rejection rates in the presence of even weak autocorrelation for series lengths often encountered in indicators developed for ecosystem-level reporting (N = 10, 20, 30). This work has broad implications for ecosystem-level reporting, where indicator time series are often limited in length, maintain a variety of error structures, and are typically assessed using a single statistical method applied uniformly across all time series.

Comparison of trend detection methods in GEV models

2020 ◽  
pp. 1-16
Author(s):  
László Németh ◽  
Zuzana Hübnerová ◽  
András Zempléni
Keyword(s):  
Trend Detection ◽  
Detection Methods
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