Trends In Time Series Research Articles

An overview of ambient sound using Ocean Observatories Initiative hydrophones.

The Ocean Observatories Initiative (OOI) sensor network provides a unique opportunity to study ambient sound in the north-east Pacific Ocean. The OOI sensor network has five low frequency (Fs = 200 Hz) and six broadband (Fs = 64 kHz) hydrophones that have been recording ambient sound since 2015. In this paper, we analyze acoustic data from 2015 to 2020 to identify prominent features that are present in the OOI acoustic dataset. Notable features in the acoustic dataset that are highlighted in this paper include volcanic and seismic activity, rain and wind noise, marine mammal vocalizations, and anthropogenic sound, such as shipping noise. For all low frequency hydrophones and four of the six broadband hydrophones, we will present long-term spectrograms, median time-series trends for different spectral bands, and different statistical metrics about the acoustic environment. We find that 6-yr acoustic trends vary, depending on the location of the hydrophone and the spectral band that is observed. Over the course of six years, increases in spectral levels are seen in some locations and spectral bands, while decreases are seen in other locations and spectral bands. Last, we discuss future areas of research to which the OOI dataset lends itself.

On directional accuracy of some methods to forecast time series of cybersecurity aggregates

Abstract Cybersecurity aggregates are numerical data obtained by aggregation on features along a database of cybersecurity reports. These aggregates are obtained by integration of time-stamped tables using some recent results of non-standard calculus. Time-series of aggregates are shown to contain relevant information about the concrete system dealt with. Trend time series is also forecasted using known data-driven methods. Although absolute forecasting of trend time series is not obtained, a directional forecasting of trend time series is achieved thence validated by means of a rolling cross validation scheme on a public database of Scareware reports.

Identification of Co-Clusters with Coherent Trends in Geo-Referenced Time Series

Several studies have worked on co-clustering analysis of spatio-temporal data. However, most of them search for co-clusters with similar values and are unable to identify co-clusters with coherent trends, defined as exhibiting similar tendencies in the attributes. In this study, we present the Bregman co-clustering algorithm with minimum sum-squared residue (BCC_MSSR), which uses the residue to quantify coherent trends and enables the identification of co-clusters with coherent trends in geo-referenced time series. Dutch monthly temperatures over 20 years at 28 stations were used as the case study dataset. Station-clusters, month-clusters, and co-clusters in the BCC_MSSR results were showed and compared with co-clusters of similar values. A total of 112 co-clusters with different temperature variations were identified in the Results, and 16 representative co-clusters were illustrated, and seven types of coherent temperature trends were summarized: (1) increasing; (2) decreasing; (3) first increasing and then decreasing; (4) first decreasing and then increasing; (5) first increasing, then decreasing, and finally increasing; (6) first decreasing, then increasing, and finally decreasing; and (7) first decreasing, then increasing, decreasing, and finally increasing. Comparisons with co-clusters of similar values show that BCC_MSSR explored coherent spatio-temporal patterns in regions and certain time periods. However, the selection of the suitable co-clustering methods depends on the objective of specific tasks.

Evolution of landscape pattern and the association with ecosystem services in the Ili-Balkhash Basin.

The Ili-Balkhash Basin in Central Asia is an arid endorheic basin shared by China and Kazakhstan. Population growth, socio-economic development, ecological conservation measures, and climate change have spurred land use and land cover changes and ecosystem services variations. This study used the long-term dataset from 1992 to 2018 to detect the landscape pattern evolution and its association with ecosystem services. The landscape pattern was quantified using landscape metrics, and the GeoDetector model quantified the driving factors of landscape pattern evolution. The ecosystem service value was assessed using the benefit transfer method. The time series trend was identified by the linear regression analysis combined with the Mann-Kendall algorithm, and Pearson's correlation coefficient was used to confirm the correlation. The temporal dynamics of the landscape pattern indicated the significant conversion of bare land to grassland. Cropland and urban land expanded significantly at the expense of forestland, grassland, and bare land. Various landscape elements tended to be more uniformly distributed across the basin with more regular shape and higher aggregation. The ecosystem service value increased significantly, and its correlation with the landscape pattern varied according to land use and land cover (LULC) types. The weakened shape complexity, the strengthened aggregation degree, and the more uniform distribution of different LULC types helped elevate total ecosystem service value. The results advanced the understanding of landscape pattern evolution and provided the scientific reference for land management regarding ecosystem services. Given the watershed ecosystem's integrity, transboundary cooperation between China and Kazakhstan was suggested to reinforce watershed sustainability through integrated watershed land resource planning and the joint adaptive strategies to climate change.

Exploring Trends in Abundance of Young‐of‐the‐Year and Age‐1 Atlantic Croaker, Black Drum, Spot, and Weakfish in Relation to Salinity, Temperature, and Large‐Scale Climatic Signals in a Mid‐Atlantic Estuary

AbstractAtlantic Croaker Micropogonias undulatus, Black Drum Pogonias cromis, Spot Leiostomus xanthurus, and Weakfish Cynoscion regalis have shown species‐specific, varying trends in abundance despite general declines in commercial landings throughout the Delaware River estuary. Identifying how environmental factors and climatic processes affect fishes at multiple life stages is needed to enhance the precision of regulatory actions for managed species. Species‐, area‐, and age‐specific indices were compared with depth, salinity, temperature, the Atlantic Multidecadal Oscillation (AMO), and the North Atlantic Oscillation (NAO) to explore potential relationships as well as temporal and spatial parameters. Our results demonstrate that station (15 indices), salinity (14 indices), the AMO and NAO (13 indices each), and depth and temperature (12 indices each) served as important components for models of abundance for 17 species‐, age‐, and area‐specific combinations from 1991 to 2016. Significant time series trends were detected in six of the modeled indices of abundance, including baywide indices of age‐1 Weakfish (decline) and age‐0 Atlantic Croaker (increase); age‐0 Atlantic Croaker (increase) and Weakfish (decline) in Delaware; and age‐1 Spot (decline) and Weakfish (increase) in Delaware. Our results demonstrate how multiple fixed‐station surveys can be combined to quantitatively assess environmental and climatic effects correlated with species‐, age‐, and area‐specific levels of abundance, suggesting that climatic signals are affecting smaller‐scale environmental variables that in turn affect relative abundance.

Smoothed dynamic factor analysis for identifying trends in multivariate time series

Abstract Ecological processes are rarely directly observable, and inference often relies on estimating hidden or latent processes. State‐space models have become widely used for this task because of their ability to simultaneously estimate the multiple sources of variation (natural variability and variance attributed to observation errors). For multivariate time series, a second aim is often dimension reduction, or estimating a number of latent processes that are smaller than the number of observed time series. Dynamic factor analysis (DFA) has been used for performing time‐series dimension reduction, where latent processes are modelled as random walks. Whereas this may be suitable for some situations, random walks may be too flexible for other cases. Here, we introduce a new class of models, where latent processes are modelled as smooth functions (basis splines, penalized splines or Gaussian process models). We implement these models in our bayesdfa r package, which uses the rstan package for fitting. After evaluating model performance with simulated data, we apply conventional models and our smooth trend models to two long‐term datasets from the west coast of the United States: (a) a 35‐year dataset of pelagic juvenile rockfishes and (b) a 39‐year dataset of fisheries catches. Our simulations demonstrate that models matching the underlying trend smoothness make better out‐of‐sample predictions, but this advantage diminishes with increasing levels of observation error. For both case studies, the best smooth trend models had higher predictive accuracy, and yielded more precise predictions, compared to the conventional approach. The smooth trend factor models introduced here offer a new approach for state‐space dimension reduction of multivariate time series. These flexible Bayesian models may be particularly useful for data that are clumped in time, for data with high signal to noise ratios and generally for data where the underlying trend is assumed to be relatively smooth.

Statistical Analysis of PM10 Concentration in the Monterrey Metropolitan Area, Mexico (2010–2018)

Air-quality monitoring and analysis are initial parts of a comprehensive strategy to prevent air pollution in cities. In such a context, statistical tools play an important role in determining the time-series trends, locating areas with high pollutant concentrations, and building predictive models. In this work, we analyzed the spatio-temporal behavior of the pollutant PM10 in the Monterrey Metropolitan Area (MMA), Mexico during the period 2010–2018 by applying statistical analysis to the time series of seven environmental stations. First, we used experimental variograms and scientific visualization to determine the general trends and variability in time. Then, fractal exponents (the Hurst rescaled range and Higuchi algorithm) were used to analyze the long-term dependence of the time series and characterize the study area by correlating that dependence with the geographical parameters of each environmental station. The results suggest a linear decrease in PM10 concentration, which showed an annual cyclicity. The autumn-winter period was the most polluted and the spring-summer period was the least. Furthermore, it was found that the highest average concentrations are located in the western and high-altitude zones of the MMA, and that average concentration is related in a quadratic way to the Hurst and Higuchi exponents, which in turn are related to some geographic parameters. Therefore, in addition to the results for the MMA, the present paper shows three practical statistical methods for analyzing the spatio-temporal behavior of air quality.

Optimization of Multisource Dynamic Model in TBO

When extracting flight data from airport terminal area, there are matters such as large volume, unclear features, and similar trend in time series. In order to deal with the related issues and to optimize the description, by combining with the TBO (Trajectory-Based Operation), an application proposed by the ICAO (International Civil Aviation Organization) in ASBU (Aviation System Block Upgrade), using multisource dynamic model to establish 4DDW (4D dynamic warping) algorithm, the multisource modeling integrated with evaluation system is proposed to realize the flight path optimization with time series characteristics and accord with the interval concept. The calculation results show that 4DDW can obtain the optimal solution for multiprofile calculation of TBO by comparing the composite trajectory deviation values and time dimension planning using the buffer and threshold values recommended by ICAO in airspace planning and flight procedure design. The results meet the requirements of high accuracy and convergence features of spatial waypoints and can improve the airport operation standards and terminal area capacity.

Non-stationary analysis for road drainage design under land-use and climate change scenarios

Frequency analysis has been the most widely used tool worldwide to dimension water-related infrastructures and evaluate flood risks. The concept of stationarity has been a common and practical hypothesis in hydrology for many years. However, in recent decades due to climate change pressure and changes in land use, it has been related to the presence of time-series trends that in hydrology indicate non-stationary effects. In this sense, the need to comprehensively address non-stationary frequency analysis has been identified. This study proposes to incorporate the non-stationary flood frequency analysis into the dimensioning process of road structures with the following objectives: i) evaluate the effect of land use on peak flow in a simulated period of 129 years, ii) evaluate covariates related to land use, and iii) evaluate covariates related to climate change. To this end, road drainage simulation exercises were carried out in three sections of the Ibagué-Cajamarca road located in Colombia. Likewise, the Generalized Additive Models for Location, Scale and Shape was implemented for the non-stationary analysis, and covariates related to climate variability were included, such as El Niño-Southern Oscillation indices (ONI12, ONI3.4, MEI, and SOI), and the Pacific Decadal Oscillation (PDO) index, as well as some related to the evolution of land use such as hydraulic conductivity, soil water storage in the root zone, and infiltration capacity represented in the curve number. The results indicate that the non-stationary analysis improves the prediction of maximum flows, and it is possible to obtain road drainage dimensioning that adjusts to climate and land-use variations.

Quantile Trend Regression and Its Application to Central England Temperature

The identification and estimation of trends in hydroclimatic time series remains an important task in applied climate research. The statistical challenge arises from the inherent nonlinearity, complex dependence structure, heterogeneity and resulting non-standard distributions of the underlying time series. Quantile regressions are considered an important modeling technique for such analyses because of their rich interpretation and their broad insensitivity to extreme distributions. This paper provides an asymptotic justification of quantile trend regression in terms of unknown heterogeneity and dependence structure and the corresponding interpretation. An empirical application sheds light on the relevance of quantile regression modeling for analyzing monthly Central England temperature anomalies and illustrates their various heterogenous trends. Our results suggest the presence of heterogeneities across the considered seasonal cycle and an increase in the relative frequency of observing unusually high temperatures.

Methodology for the formulation of a set of measures for the development of rural areas

Relevance. The current state of rural areas is characterized by poor development of engineering and social infrastructure, low, compared to urban, standard of living, negative forms of migration, natural decline and depopulation of the rural population. Within the framework of state regulation of the process of overcoming the current situation, state support is provided for measures for the development of rural areas put forward on the basis of local initiatives (ofmunicipalities, citizens, professional communities). However, generally accepted scientific recommendations for the formulation of such measures are not currently developed, so the topic of the study is relevant.Methods. The research is based on systematic, dialectical, abstract-logical, and organizational approaches. The object of the study is rural territories, the subject is theoretical and methodological issues related to the formulation of measures for their development.Results. A methodological approach to the formulation of a set of measures for the development of rural areas, taking into account the opportunities provided by the current state programs, is proposed. It includes the following stages: analysis of the state of the rural territory; forecasting the directions and dynamics of its development; determination of objective needs and selection of the most appropriate changes in the properties of the rural territory; formulation of a set of measures and justification of mechanisms for their implementation. In the proposed methodological approach, the rural territory is considered not in isolation, but in a single system of settlement “city — village” based on the use of SWOT and PEST analysis. At the stage of forecasting the directions and dynamics of rural development, a comprehensive use of the normative resource method and the method of extrapolation of time series trends is proposed. The selection of the most appropriate changes in the properties of the rural territory is carried out on the basis of a multi-level approach that allows to identify the problems of rural settlements, individual social groups and rural residents. It is proposed to justify the mechanisms for implementing measures for the development of rural areas based on the resources of municipalities, rural population, private business and state support measures.

A novel adaptive discrete grey prediction model for forecasting development in energy consumption structure—from the perspective of compositional data

PurposeThe purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for forecasting compositional data.Design/methodology/approachDue to the existing grey prediction model based on compositional data cannot effectively excavate the evolution law of correlation dimension sequence of compositional data. Thus, the adaptive discrete grey prediction model with innovation term based on compositional data is proposed to forecast the integral structure of China's energy consumption. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of China's energy consumption structure is conducted into a future horizon from 2021 to 2035 by using the model.FindingsChina's energy structure will change significantly in the medium and long term and China's energy consumption structure can reach the long-term goal. Besides, the proposed model can better mine and predict the development trend of single time series after the transformation of compositional data.Originality/valueThe paper considers the dynamic change of grey action quantity, the characteristics of compositional data and the impact of new information about the system itself on the current system development trend and proposes a novel adaptive discrete grey prediction model with innovation term based on compositional data, which fills the gap in previous studies.

Vulnerability of Communities to Climate Change Induced Disaster Risks and Potential Mental Health Outcomes in Isiolo County, Kenya

Purpose: The study was conducted to identify and evaluate disaster risks and mental health outcomes caused by extreme climate events. Methodology: Quantitative data was obtained from existing climate and mental health (1984-2019) records, while qualitative data was obtained from literature review of case studies and content analysis, Focus Group Discussion and household survey in four major zones for two consecutive years. ArcGIS software method explored various properties of the climate systems to infer the distribution of climate parameters, select extremes value and calculate linear trend of time series. The quantitative data was analyzed using statistical tools in Excel, IBM SPSS version 20 while climate data analysis was done using R software (version 3.21). Results: The exceedance threshold of 𝜇 = 340 𝑚𝑚 was chosen. On the other hand, mean exceedance threshold of 𝜇 = 36.50𝐶 and 𝜇 = 11.380𝐶 for minimum and maximum temperatures respectively. The rainfall band was very high or very low, deemed to create disaster risks. The results revealed that the most common disaster risks include: drought and heatwaves, strong sand storms, flash floods and floods. The duration of time, frequency and unpredictable weather variability events were above critical threshold, hence categorized as high risk, rated 1, hence fatal. Unique Contribution to Theory and Practice: The study provides historical empirical data on hazard mapping and mental health outcomes to enable policy and programmes formulation by state and nonstate actors. The study recommends development of robust environmental health procedures to diagnose mental disorders, mapping of disasters; mental disorder epidemiology and make it user friendly to advice policy, scale up solutions and accelerate evidence informed advocacy on adaptation and resilience mental health programme strategies

Adaptability of Financial Time Series Prediction Based on BiLSTM

Accurate prediction of financial market can promote the steady development of financial market, but the high frequency and high noise of financial time series make accurate prediction a challenging task. In this paper, bidirectional long short-term memory neural network (BiLSTM) in deep learning is applied in financial time series, and BiLSTM has one more layer of reverse structure to try to mine more effective information. The prediction performances of unidirectional long short-term memory neural network (LSTM), support vector regression (SVR) and differential autoregressive moving average model (ARIMA) are compared. The results show that BiLSTM model has the highest prediction accuracy, which can fully capture the past and future data information simultaneously, take the reverse relationship of data into account, and predict the long-term and short-term dynamic trends of financial time series effectively.

Monitoring Persistent Coal Fire Using Landsat Time Series Data From 1986 to 2020

Coal fires pose great threats to valuable energy resources, the ecological environment, and human safety. They are one of the most persistent fires on the Earth, which can burn for an extremely long-term period from decades to hundreds or even thousands of years. Remote sensing detection of coal fires is of significance for mitigating coal fire hazards. Nevertheless, short-term or temporal discrete land surface temperature (LST) data have limited capability in characterizing the persistent coal fire. This study proposed a methodology to monitor persistent coal fires using long-term Landsat thermal images and further to analyze spatiotemporal dynamics of coal fires. A total of 1118 high-quality Landsat images (each image containing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$446\times446$ </tex-math></inline-formula> pixels) spanning 35 years from 1986 to 2020 in the Wuda coalfield area (China) were processed to retrieve the LST. LST time series of each pixel was decomposed into the seasonal, trend, and remainder components. Coal fire areas were demarcated by using the range of the trend components. To trace the trend and change point of the LST time series, the Mann–Kendall test was applied to the trend components, and the Pettitt test was employed to the original time series vectors of those pixels located in the coal fire areas, respectively. The random sample consensus algorithm was utilized to identify the background temperature (inliers) and high temperature (outliers) and, thus, judge the coal fire burning period, and the symbolic aggregate approximation algorithm was used to evaluate the robustness of the judgment. The calibration was conducted according to the filed surveys, obtaining spatiotemporal 3-D coal fire dynamics. The proposed methodology was testified by comparisons with fieldwork and regional anomaly extractor algorithm, demonstrating good performance in comprehensive monitoring of persistent coal fires.

Trends summarization of times series: a multi-objective genetic algorithm-based model

Aim: The explosion of the amounts of data generated in many application domains, makes the paradigm of data summarization more essential. Furthermore, it is of a great interest to effectively handle some specific needs. In this work, we discuss an advanced model to drive linguistic summarization in the context of time series. This model relies on a multi-objective genetic algorithm mechanism to generate a set of best summaries from a large number of candidates. Methods: To achieve this objective, the current work is divided into two parts: The first part is dedicated for extracting the linguistic summaries of the dynamic characteristics of the trends of time series. It is achieved using the traditional genetic algorithm where the fitness function represents the truth degree of the linguistic quantified proposition. The second part is devoted to formalise the problem of interest as a multi-criteria optimization problem. We use different quality measures of summary as targets for improving the predicted set of summaries. To reach this goal, we use the Fast Non-Dominated Sorting Genetic Algorithm NSGA-II. Results: We evaluate the proposed approach on real data from a Smart Campus application (Neocampus project of the University of Toulouse, France). The results are promising and confirming the usability of the proposed approach. Conclusion: The proposed approach overcomes the problem of the overabundance of irrelevant linguistic summaries of the time series. It allows selecting a set of best summaries regarding some relevant criteria.

Climate Variability Patterns and Farmers’ Perceptions of Its Impact on Food Production: A Case Study of the Gelda Watershed in the Lake Tana Basin in Northwest Ethiopia

This study looked at patterns of erratic climate and farmer perceptions in the Gelda watershed of Lake Tana Basin in northwestern Ethiopia. The National Meteorological Agency of Ethiopia provided essential climatic data for the period between 1981 and 2011. A household survey and focus group discussions were also used to understand about local communities’ perceptions of climate variability and its impact on food production. Time series trend analysis of observed rainfall and temperature conditions was detected using linear regression analysis. To compare the means of climatic parameters and determine whether the average difference was significantly different from zero, the paired sampled t-test was used. The study found that the average annual temperature trend increased by 1.1°C, while the amount and distribution of annual and monthly rainfall decreased, varied across the catchment, and fluctuated during the study periods (1981–2011) at mean temperature rise with an average rate of 0.17°C in the last decade. The variability of annual and monthly rainfall in terms of intensity and distribution has decreased and varied across the watershed. The analysis revealed that annual rainfall variability was variable in the upper catchment (CV &gt; 11.7%) and lower catchment (CV &gt; 14.4%). The amount and intensity of temperature, on the other hand, increased throughout the study watershed, despite observed variation both spatially and temporally (stated decades). Farmers’ understanding and expression of climate variability in terms of erratic rainfall distribution, decreasing amount, and increasing temperature over the last three decades, however, matched the observed data. Moreover, farmers are pointed out that high population pressure; deforestation and intensified agriculture are responsible factors for the variability of climate in the study watershed. Therefore, based on the findings, scientist and policymakers has to design appropriate adaptation measures that can tackle the aggravation of climate variability for future.

Impact of redistributing deaths by ill-defined causes in oral and oropharyngeal cancer mortality in Brazil.

Less-than-optimal reliability of mortality information systems regarding the underlying cause of death can mask the reality of oral (OC) and oropharyngeal cancer (OPC) mortality. This study aimed to assess the impact on the magnitude and temporal trends of OC and OPC mortality in Brazil of two statistical approaches to redistribute deaths with ill-defined underlying causes. We analyzed deaths with ill-defined causes in Brazil by macro-region, between 1996-2018. The Mortality Information System provided official information on deaths. Two correction methods were applied: the EF method, which proportionally reallocates deaths classified as R00-R99 in the ICD-10 to the remaining specific causes of death according to the proportion of deaths with certified causes; and the GBD method, which considers the concept of garbage codes, redistributing deaths from several ICD-10 chapters according to previously established coefficients. For the trend analysis of mortality (certified and redistributed), the Prais-Winsten method was carried out. The OC and OPC death rates had an evident increase after the redistribution by the two techniques in all regions of the country; the increase was higher using the GBD method. In the Northeast and North regions, this method more than doubled the certified death rates. The redistribution methods also changed time series trends. In epidemiological studies of mortality from OC and OPC, it is necessary to redistribute deaths from ill-defined causes when analyzing data from less-than-optimal information systems. The choice of the correction method is critical; epidemiological studies must manage it as a methodological decision that has significant impacts on results.

Using Chaos theory fundamentals for analysing temperature, precipitation variability and trends in Northern Patagonia, Argentina

The fundamentals of Chaos theory allow the study of climatic conditions and long-term modifications produced by changes in their spatial and temporal scales. The aim of this work is to analyse the variability and changes produced in the annual cycles of temperature and precipitation in Northern Patagonia, Argentina, applying multifractal analysis as a practical mathematical tool of Chaos theory. Data from the NASA POWER Project (2021) was implemented as an alternative dataset for carrying out climatological studies in the area. Annual mean temperature and precipitation time-series data (1981–2019) were analysed at 72 grid points with 1° of spatial resolution. The Mann–Kendall test was used to calculate the trends through the annual cycles of the meteorological variables. Fractal dimension values were calculated using Multifractal Detrended Fluctuation Analysis. The Hurst exponent, complexity and asymmetry were the multifractal dimensions describing the persistence of time-series trends and climatic variability. The results showed changes in the annual cycles of both variables during the study period. The most significant finding was a large area in the centre and north of the study area, where the decrease in the rainfall regime was persistent. The Hurst exponent detected a sector in the Patagonian Andes mountain range where the temperature increase was constant. This work demonstrates that fractal geometry is useful to describe meteorological variability and obtain better short-, medium- and long-term forecasts.

Trends in Suicide among Lesbian, Gay, and Bisexual Hispanic Individuals: National Violent Death Reporting System Data from 2012-2016

ABSTRACT Due to multiple minority stressors, persons who are Hispanic and lesbian, gay or bisexual (LGB) can experience an increased risk of injury and death, including suicide. We examined trends and correlates of deaths by suicide among LGB Hispanic individuals using data from the National Violent Death Reporting System from 2012 to 2016 (N = 1,132). A multilevel mixed-effects logistic regression model examined time-series trends associated with suicide between LGB and heterosexual individuals, controlling for sociodemographic and clinical characteristics. Over time, increases in suicide were larger among LGB Hispanic individuals. Increased odds associated with suicide were also attributed to women, individuals with a higher education level, those who experienced mental health problems, those with alcohol-related problems, and individuals with a history of mental illness. LGB Hispanic individuals continue to see an increased risk for suicidality. Health intervention and policy efforts should focus on the multiple minority stressors experienced by this population.