Trends In Series Research Articles

NDVI Variation and Yield Prediction in Growing Season: A Case Study with Tea in Tanuyen Vietnam

Tea is one of the most significant cash crops and plays an important role in economic development and poverty reduction. On the other hand, tea is an optimal choice in the extreme weather conditions of Tanuyen Laichau, Vietnam. In our study, the NDVI variation of tea in the growing season from 2009 to 2018 was showed by calculating NDVI trend and the Mann-Kendall analysis to assess trends in the time series. Support Vector Machine (SVM) and Random Forest (RF) model were used for predicting tea yield. The NDVI of tea showed an increasing trend with a slope from −0.001–0.001 (88.9% of the total area), a slope from 0.001–0.002 (11.1% of the total area) and a growing rate of 0.00075/year. The response of tea NDVI to almost climatic factor in a one-month time lag is higher than the current month. The tea yield was estimated with higher accuracy in the RF model. Among the input variables, we detected that the role of Tmean and NDVI is stronger than other variables when squared with each of the independent variables into input data.

Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

Vegetation phenology dynamics as an indicator of energy and productivity functions in semi-arid savannah protected areas: a case study of Gonarezhou National Park in south-eastern Zimbabwe

Spatial and temporal patterns of vegetation productivity in semi-arid savanna national parks are influenced by differences in land cover and changes in time series trends. The main purpose of this paper is to analyse patterns of vegetation productivity metrics of base value, peak value, amplitude, and small and large integrals in Gonarezhou National Park (GNP) in south-eastern Zimbabwe from 1981 to 2015. Three sample sites comprising shrublands, deciduous broadleaved forested woodlands and mixed cover (shrublands, broadleaved deciduous forested woodlands and grasslands) were selected to show existing patterns of vegetation productivity for GNP. We used remotely sensed Normalised Difference Vegetation Index (NDVI) data which was further processed in the TIMESAT 3.3 program to derive productivity metrics. We then tested differences in land cover using analysis of variance and changes in time-series trends using Mann–Kendall and Theil–Sen’s tests. We note significant differences in land cover (P < 0.01) in selected samples. There are significant downward trends in the base value in shrublands (P < 0.01) and broadleaved deciduous forested woodlands (P = 0.04). Significant upward trends in the amplitude in the shrublands (P < 0.01) and mixed cover areas (P = 0.01) were noted. However, there are no changes in vegetation productivity, as indicated by the peak value and large and small integral indices. Shrublands are becoming vulnerable in terms of energy and vegetation productivity and need constant monitoring. Long-span coarse-resolution images are important stepping stones in providing a baseline for further studies from moderate and fine-resolution imagery. Research on vegetation productivity using fine-resolution imagery is more suitable for GNP.

Pathways to drug prevalence estimation: synthesizing three comments on triangulation.

Pathways to drug prevalence estimation: synthesizing three comments on triangulation.

CONCEPT TO ANALYZE THE DISPLACEMENT TIME SERIES OF INDIVIDUAL PERSISTENT SCATTERERS

Abstract. Persistent Scatterer Interferometry (PSInSAR) exploits a time series of Synthetic Aperture Radar (SAR) images to estimate the mean velocity with which the surface of the earth is deforming. However, most PSInSAR algorithms estimate the mean velocities using a linear regression model. Since some deformation phenomena can exhibit a more complex behavior over time, using a linear regression model leads to potentially wrong estimations for the mean velocity. For example, the velocity of a landslide moving down a steep slope can change depending on the water content of the material of the landslide, or an inactive landslide can reactivate due to an earthquake. Both scenarios would not result in a time series with a constant linear slope but in a piecewise linear time series.This paper presents a Matlab-based tool to analyze an individual Persistent Scatterer (PS) time series. The Persistent Scatterer Deformation Pattern Analysis Tool (PSDefoPAT) aims to build a mathematical model that sufficiently describes the time series trend and seasonal and noise components. The trend component is estimated using polynomial regression and piecewise linear models, while a sine function approximates the seasonal component. The goal is to identify the best fitting model for the displacement time series of a PS. PSDefoPAT is introduced by examine the time series of three different PS located in the region surrounding Patras, Greece. Based on the derived models, we discuss the nature of their deformation patterns.

A Statistical Analysis of Daily Snow Depth Trends in North America

Several attempts to assess regional snow depth trends have been previously made. These studies estimate trends by applying various statistical methods to snow depths, new snowfalls, or their climatological proxies such as snow water equivalents. In most of these studies, inhomogeneities (changepoints) were not accounted for in the analysis. Changepoint features can dramatically influence trend inferences from climate time series. The purpose of this paper is to present a detailed statistical methodology to estimate trends of a time series of daily snow depths that account for changepoint features. The methods are illustrated in the analysis of a daily snow depth data set from North America.

Two-stage prediction of machinery fault trend based on deep learning for time series analysis

Fault prediction technology provides a way to reduce the loss caused by equipment failure. Currently, many efforts of failure prediction pay more attention to the fault trend and the remaining useful life. This paper devotes to predicting a specific failure mode of device in the future. We propose a two-step prediction method with two sub-models to predict the fault mode. The first model, named regression model, combines Attention-based-LSTM (Long Short-Term Memory) and Random Forest (RF) to predict time series trends, where wavelet packet analysis is adopted to extract elementary features of time series and form multivariable time series. Based on the status data forecasted by the first model, the second model, named classification model, combines Attention-based-LSTM and Extra-Tree (ET) to classify fault mode. The classified fault will occur in the device at some point in the future. In the regression model, Attention-based-LSTM performs deep level feature extraction of vibration sensor signals, and the result is fed to the last layer (i.e., an RF layer), which takes responsibility for predicting the future sequence. The future sequence is then provided to the classification model, where Attention-based-LSTM is used to extract implicit features, and ET classifier is used to accept these features and figure out the failure phenomenon. Our approach for predicting failure modes includes the severity and type of fault, which are rarely investigated synthetically. The experimental results of bearing data show that the sub-models can achieve high prediction accuracy and our method can predict the future failure mode with precision.

Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model.

Novel coronavirus (COVID-19) was discovered in Wuhan, China in December 2019, and has affected millions of lives worldwide. On 29th April 2020, Malaysia reported more than 5,000 COVID-19 cases; the second highest in the Southeast Asian region after Singapore. Recently, a forecasting model was developed to measure and predict COVID-19 cases in Malaysia on daily basis for the next 10 days using previously-confirmed cases. A Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) is proposed by establishing L and ET parameters via several tests. The advantage of using this forecasting model is it would discriminate noise in a time series trend and produce significant forecasting results. The RF-SSA model assessment was based on the official COVID-19 data released by the World Health Organization (WHO) to predict daily confirmed cases between 30th April and 31st May, 2020. These results revealed that parameter L = 5 (T/20) for the RF-SSA model was indeed suitable for short-time series outbreak data, while the appropriate number of eigentriples was integral as it influenced the forecasting results. Evidently, the RF-SSA had over-forecasted the cases by 0.36%. This signifies the competence of RF-SSA in predicting the impending number of COVID-19 cases. Nonetheless, an enhanced RF-SSA algorithm should be developed for higher effectivity of capturing any extreme data changes.

Resurgence of Subacute Sclerosing Panencephalitis: Case Series and Global Epidemiological Trends

AbstractResurgence of subacute sclerosing panencephalitis (SSPE)—case series and global epidemiological trends. We noted a recent increase in cases of SSPE admitted in our institute, even though they had received measles vaccination. We did a detailed study of our cases and compared with global epidemiological trends of SSPE in preimmunization and era of developed immunity. Out of total 23 cases of SSPE, 12 presented in the year 2017 alone, reflecting a steep rise in incidence. Sixteen patients had received measles vaccine and never had prior measles infection. Mean age of onset was 8.2 years and average time of progression to advanced stage of disease was 65 days. Global data showed similar trends, that is, earlier age of onset with a faster rate of progression in the postvaccination era as compared with prevaccine era. Possible mechanisms to account for this trend include an early wild measles infection in the critical age of 6 to 9 months, before vaccination. There is a changing epidemiological trend of SSPE in terms of lower age of onset and faster rate of progression, also reflected in global data. There is a need for multicenter studies to verify the findings and explore possible measures like lowering the age of measles vaccination to halt this alarming trend.

Estimation obtaining instrumental means based on nonlinear dynamics methods

This article presents the results of the adapted complex methodology operation for the time series dynamics estimating, its features are in the joint use of both classical and new “nonlinear” statistics. The methods proposed and tested by the author are presented in the form of a pre-estimating and estimating model for assessing the grain yields time series trend stability in the Volgograd region (1930–2019) and obtaining a forecasting. The following methods of nonlinear dynamics were tested: the Hurst normalized range method, phase-plane analysis, and a linear cellular automaton. The results of analysis and forecasting on real yield data are presented in the form of the agro-economic system modeling lower level values, which in turn are input data for models of the upper level - the agro-economic system management level.

Tourist and recreational complex elements dynamics research data analysis qualitative methods

This article presents the results the adapted complex methodology operation for the pre-estimating analysis of the dynamics of tourist flow time series decomposition in the Dombai ski village, the features of which are in the combined use of both classical and new “nonlinear” statistics. The methods proposed and tested by the authors are presented in the form of a pre-estimating model for assessing the tourist flow time series trend stability. The following methods of nonlinear dynamics were tested: the method of the normalized Hurst range, phase-plane analysis. The methods of fractal analysis used and adapted by the authors ensure the identification and assessment of a number of socio-economic time series fundamental qualitative and quantitative pre-estimating characteristics, namely, the presence of memory, including long-term memory, its depth, which in turn can determine the process as persistent (antipersistent, trend-resistant or reverse) to reveal the noise color.

Spatiotemporal variability and time series trends of rainfall over northwestern parts of Ethiopia: the case of Horro Guduru Wollega Zone.

This study examined the variability and trend of rainfall over Horro Guduru Wollega Zone. Studies such as this have paramount importance in countries and areas where rain-fed agriculture is predominant. Datasets for analysis were obtained from the National Meteorological Agency of Ethiopia (NMA) from 1987 to 2016 and Climate Hazard Group InfraRed Precipitation (CHIRPS) 1987-2019 with the station portal. Monthly rainfall with temporal variability ranging from 9.77 to 141.93% was observed. High variable (CV > 30%) and less variable (CV < 20%) rainfall was observed in the CHIRPS data records. Rainfall during most months of the last 30 and 33years showed a decreasing trend. Rainfalls with temporal variability ranging from 12.7 to 75.92% and from 8.11 to 43.45% were observed during the 3-month seasons, respectively. Rainfall over the 3-month seasons of the last 30 and 33years showed a decreasing trend. The average total rainfall ranging from 107.203 to 1016.82mm and from 122.8 to 1147.9mm, with variability from 9.163 to 55.7% and from 7.831 to 36.68% were observed during the Belg, Kiremt, and Bega seasons of the last 30 and 33years, respectively. A decrease in rainfall was tested over these three seasons of the last 30 and 33years. Significantly different (P < 0.05) and less variable (CV < 20%) annual total rainfall was recorded at 24 stations over 30years. Declining annual rainfall was observed over 30 and 33years. Non-significantly different (P < 0.05) and less variable (CV < 20%) average decadal rainfall ranging from 1342.6 to 1372.8mm was observed during the last 33years. The study area had experienced a rainfall with decreasing trends almost over all time scales. These might have been resulting in failure of agricultural production that necessitates developing and implementing systematic planning and management activities in the crop calendar under the face of changing rainfall patterns.

New PV Performance Loss Methodology Applying a Self-Regulated Multistep Algorithm

Analyses of performance loss rates in photovoltaic (PV) systems are not yet standardized, and are typically carried out by a linear regression of the evolution of a certain performance metric (e.g., performance ratio, yield, etc.) over time. In this article, we propose a novel methodology of advanced PV system performance loss rate modeling applying a self-regulated multistep algorithm. The developed algorithm automatically detects the number and positions of breakpoints in nonlinear performance time series and divides the performance trend into an adequate number of linear segments. Instead of calculating one linear performance loss rate, given in percentage per year, as is common practice, multiple linear performance loss values are determined, depending on the trend of the time series and subsequently the number of breakpoints. The algorithm is fully automated. We have applied our methodology on data of an experimental PV installation in Bolzano/Italy, which consists of 26 different PV systems. The overall linear performance loss rate of the facility's crystalline silicon systems is between -0.5 and -1.3%/year, whereas the thin-film PV systems experience values between -0.6 and -2.4%/year. Based on our results, the algorithm appears to be stable and accurate. The methodology is to be used as a fast and automated check of PV systems in operation to detect anomalies affecting performance (in an early stage). By building up a large database of detected issues in the field this algorithm will enable us to better understand the performance evolution of different PV system types in varying climates.

Dynamic interpretation of the factors causing dam deformation with hybrid grey dynamic incidence model

Deformation measured from various measuring points within and on a dam provides important information on the structural behaviour. The effective analysis of the development trend of factors causing deformation is a significant prerequisite for the structural health monitoring of dams. However, the existing research, focuses on qualitative analysis without distinguishing the difference in the contribution of deformation-influencing factors and revealing their dynamic evolution. Additionally, the quantitative analyses concentrated on single-point information cannot reflect the global correlation between the overall deformation and environmental factors, and the correlation cannot be fully characterised either by the fluctuation range or changing trend of monitoring data series. To overcome these issues, a novel hybrid grey dynamic incidence model is designed by integrating the Deng’s grey incidence model and grey dynamic trend incidence model to dynamically analyse the factors causing dam deformation. In particular, the classic grey incidence models are improved through dimensional expansion for multi-point monitoring environment. Moreover, considering the effect of fluctuation range and changing trend, the hybrid grey dynamic incidence degree is defined to improve the interpretability of the influencing variables. Hence, the proposed model is applied to dynamically quantify the global connections of environmental factors against the deformation of an arch dam. The results are verified to be effective in improving the prediction accuracy in the structural safety of a dam and can be used to diagnose the working state of individual instrument location.

Assessment of properties of vegetation phenology in fire-affected areas from 2000 to 2015in the Peloponnese, Greece

Fire events are among the natural disasters that affect both ecosystem functioning but also human lives. The present work deals with the assessment of the properties of vegetation phenology in areas affected by fire events during a 10-year period in the Peloponnese, Greece. The main objective is to identify special patterns in NDVI time series of fire-affected areas, that can be used either as precursory indicators or as a tool for assessment of regeneration patterns. Therefore, we constructed and analyzed time series of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI at 250 m spatial resolution with a 16-day time step for affected and non-affected areas surrounding the burnt sites, where the majority of the area refers to the same land cover type. Analysis of changes of NDVI time series of both burnt and unburnt areas was conducted and results indicated that all fire events in the burnt sites caused breaks in the NDVI time series, demonstrated either as NDVI shifts to the lower and/or as changes in the trend and seasonality of the time series. Trends of the NDVI time series of the affected sites were almost doubled in the post fire period compared to the period before the event, indicating the evolution of the regenerating process. The basic statistics of the NDVI time series of the affected and non-affected areas correspond to greater minimum NDVI values of the affected areas throughout the study period, except of the two years after the fire event. Mean NDVI values were almost equal in burnt and unburnt areas before the events and reached the same values approximately seven years after the fire. Maximum NDVI values of the affected and non-affected sites were almost equal before the event but were abruptly reduced in the affected areas after the events and revert to their original state approximately ten years after. An alarming finding is the considerably lower standard deviation of the fire affected areas in comparison to non-fire-affected ones, during all years of the study period, except of a short period immediately after the fire event. It seems thus that areas covered by homogeneous vegetation with no fire breaks or other landforms to act as barriers to fire are the most vulnerable and most severely affected.

The 2019/2020 floods in the UK: a hydrological appraisal

The 2019/2020 floods in the <scp>UK</scp>: a hydrological appraisal

Impact of the COVID-19 pandemic and related control measures on cancer diagnosis in Catalonia: a time-series analysis of primary care electronic health records covering about five million people

ObjectiveCancer care has been disrupted by the response of health systems to the COVID-19 pandemic, especially during lockdowns. The objective of our study is to evaluate the impact of the...

Statistical Analysis and Trend Detection of the Hydrological Extremes in the Váh River at Liptovský Mikuláš

Abstract Natural climate fluctuation, as well as expected climate change, brings additional water regimes in the flow of a number of serious issues and uncertainties. The upper parts of the river basins are suitable for studying the effect of potential climate change or increased air temperature on drainage conditions in the basin. The Váh River is the biggest left-side Danube River tributary and the second biggest river in Slovakia. Gauging station Váh – Liptovský Mikuláš is the final profile above the water reservoir Liptovská Mara, one of the largest reservoirs in Slovakia. The contribution deals with the trend analysis of the extreme flows regime and the waves volume belongs to the annual maximum flow at gauging station Váh – Liptovský Mikuláš in a selected time period (1931–2015). Consequently, the trend analyses of precipitation depth and air temperature have been made at three selected meteorological stations located in the upper part of the Váh River basin. We have used the Mann-Kendall nonparametric test, which is one of the most widely used nonparametric tests to detect significant trends in a time series.

Variability and time series trend analysis of rainfall and temperature in Dramaga Sub-District, Bogor, Indonesia

Human-induced climate change is considered a threat to communities and the natural system, in particular, agriculture, forestry, economy, health, and well-being. Explicitly, extreme weather events namely flood and long dry season, are great concerns for Indonesia and the region. The purpose of this study is to detect rainfall and temperature trends in the Sub-district of Dramaga for the past thirty years. Dramaga has a huge number of populations relying on rain-fed agriculture, to feed themselves and support food security. Monthly rainfall and temperature data were obtained from the Dramaga Climatology Station of Meteorology, Climatology, and Geophysics Agency from the period 1990-2019. A Mann-Kendall statistical test has been run to detect the statistical significance of time series in comparison with the linear model. The findings indicated no significant trends in the seasonal and annual rainfall in the past thirty years, whereas, annual mean and maximum temperature are rising significantly. Further, the annual minimum temperature does not show a statistically significant increase. Climate events owing to the rainfall variation and Temperature rise threaten smallholder farmers. Results of the study might help decision-makers for climate-smart agricultural projects development at the community level. As to be able, to access environmentally sustainable self-sufficient agriculture.

Prediction of Epidemic Trends in COVID-19 with Mann-Kendall and Recurrent Forecasting-Singular Spectrum Analysis

Novel coronavirus also known as COVID-19 was first discovered in Wuhan, China by end of 2019. Since then, the virus has claimed millions of lives worldwide. In 29th April 2020, there were more than 5,000 outbreak cases in Malaysia as reported by the Ministry of Health Malaysia (MOHE). This study aims to evaluate the trend analysis of the COVID-19 outbreak using Mann-Kendall test, and predict the future cases of COVID-19 in Malaysia using Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) model. The RF-SSA model was developed to measure and predict daily COVID-19 cases in Malaysia for the coming 10 days using previously-confirmed cases. A Singular Spectrum Analysis-based forecasting model that discriminates noise in a time series trend is introduced. The RF-SSA model assessment is based on the World Health Organization (WHO) official COVID-19 data to predict the daily confirmed cases after 29th April until 9th May, 2020. The preliminary results of Mann-Kendall test showed a declining trend pattern for new cases during Restricted Movement Order (RMO) 3 compared to RMO1, RMO2 and RMO4, with a dramatic increase in the COVID-19 outbreak during RMO1. Overall, the RF-SSA has over-forecasted the cases by 0.36%. This indicates RF-SSA’s competence to predict the impending number of COVID-19 cases. The proposed model predicted that Malaysia would hit single digit in daily confirmed cased of COVID-19 by early-June 2020. These findings have proven the capability of RF-SSA model in apprehending the trend and predict the cases of COVID-19 with high accuracy. Nevertheless, enhanced RF-SSA algorithm should to be developed for higher effectivity in capturing any extreme data changes.