To see the other types of publications on this topic, follow the link: Distributed data series index.
Journal articles on the topic 'Distributed data series index'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Distributed data series index.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Chatzakis, Manos, Panagiota Fatourou, Eleftherios Kosmas, Themis Palpanas, and Botao Peng. "Odyssey: A Journey in the Land of Distributed Data Series Similarity Search." Proceedings of the VLDB Endowment 16, no. 5 (2023): 1140–53. http://dx.doi.org/10.14778/3579075.3579087.
Full textAbstract:
This paper presents Odyssey, a novel distributed data-series processing framework that efficiently addresses the critical challenges of exhibiting good speedup and ensuring high scalability in data series processing by taking advantage of the full computational capacity of modern distributed systems comprised of multi-core servers. Odyssey addresses a number of challenges in designing efficient and highly-scalable distributed data series index, including efficient scheduling, and load-balancing without paying the prohibitive cost of moving data around. It also supports a flexible partial replication scheme, which enables Odyssey to navigate through a fundamental trade-off between data scalability and good performance during query answering. Through a wide range of configurations and using several real and synthetic datasets, our experimental analysis demonstrates that Odyssey achieves its challenging goals.
2
Liu, Feng-Chi, Cathy W. S. Chen, and Cheng-Ying Ho. "Bayesian Forecasting of Bounded Poisson Distributed Time Series." Entropy 26, no. 1 (2023): 16. http://dx.doi.org/10.3390/e26010016.
Full textAbstract:
This research models and forecasts bounded ordinal time series data that can appear in various contexts, such as air quality index (AQI) levels, economic situations, and credit ratings. This class of time series data is characterized by being bounded and exhibiting a concentration of large probabilities on a few categories, such as states 0 and 1. We propose using Bayesian methods for modeling and forecasting in zero-one-inflated bounded Poisson autoregressive (ZOBPAR) models, which are specifically designed to capture the dynamic changes in such ordinal time series data. We innovatively extend models to incorporate exogenous variables, marking a new direction in Bayesian inferences and forecasting. Simulation studies demonstrate that the proposed methods accurately estimate all unknown parameters, and the posterior means of parameter estimates are robustly close to the actual values as the sample size increases. In the empirical study we investigate three datasets of daily AQI levels from three stations in Taiwan and consider five competing models for the real examples. The results exhibit that the proposed method reasonably predicts the AQI levels in the testing period, especially for the Miaoli station.
3
Triebel, Dagmar, Camila Uribe-Holguin, Stefan Seifert, Markus Weiss, and Peter Scholz. "Connecting IndExs Editors and exsiccata IDs with Wikidata for Disambiguation of People Names and Work in Botanical and Mycological Collections." Biodiversity Information Science and Standards 6 (August 23, 2022): e93585. https://doi.org/10.3897/biss.6.93585.
Full textAbstract:
"IndExs—Index of Exsiccatae" is an online database with bibliographic information on exsiccatae and exsiccata-like series launched in 2001 (Triebel and Scholz 2022). This type of series is a specific system in botany and mycology to create, publish and distribute well identified and documented reference material. In most cases the distributed specimens are numbered and each number consists of uniform material (herbarium duplicates) from a single collection event. Exsiccatal series are regularily published with small booklets containing the printed labels/ schedae of each numbered entity. The title of the series often shows the geographic and taxonomic focus of the series, e.g., "Delogne & Gravet, Hépat. Ardenne" and "Hertel, Lecideaceae Exs.". The persons editing the series are specialists, often recognized botanists and taxonomists. They are mostly not identical with the persons collecting and identifying the specimens distributed. Examples are E. M. Fries with "Fries, Herb. Norm. Pl. Suec.", G. L. Rabenhorst who published 24 series with more than 6,000 numbered entities and K. H. Rechinger with "Rechinger & Polunin, Exs. Herb. Baghdad". In the minority of cases the editors are anonymous persons and organisations devoted to plant exchange like "Société Dauphinoise pour l'échange des plantes". The more than 2.200 known series are widely distributed in public herbaria, either kept separately or integrated in the main collection. We estimate that more than 10 million specimens belong to such a series with printed labels. Approximately 70 series are running.The eldest exsiccata might be that of Johann Balthasar Ehrhart (from 1732 see here). It is followed by the better known exsiccatae edited by Jakob Friedrich Ehrhart, e.g., the series "Ehrhart, Pl. Crypt. Linn." starting with 1785. The two newest series started in 2020 and are bryophyte series from Taiwan and Vietnam.The online database IndExs categorizes the series according to the group of organisms distributed and delivers editors, full title, standard abbreviation, editing institution, place of publication, range of (suggested) publication dates, range of numbered entities, examplary images of printed label as well as information sources and literature (Triebel et al. 2004). A stable and persistent exsiccata identifier, so-called "IndExs Exsiccata ID" is given. This set of standardized information is available via the IndExs search interface, and ready to be downloaded via several formats (csv, xls, xml). A machine readable REST web service is under development. IndExs information and services with stable IndExs Exsiccata ID are used by data portals like the Macroalgal Herbarium Consortium Portal powered by Symbiota, the JACQ herbarium management system and integrated in collection management systems like DiversityCollection, a module of the Diversity Workbench (DWB) tool suite. It is envisaged to be included in future terminology services like the GFBio Terminology Service. IndExs is appropriate to build the curated reference list for exsiccatae in the frame of herbarium digitization approaches (Borsch et al. 2020). IndExs is storing information on the work of 1,300 editors of exsiccatae who are persons from nearly 300 years. According to the data models of DiversityAgents (Weiss et al. 2016) and DiversityExsiccatae (Hagedorn et al. 2008) the information is managed in freely accessible interlinked instances of SQL RDBMS DiversityAgents and DiversityExsiccatae. The applications are installed as part of the data network at the SNSB IT Center.In 2012, the Wikidata project started and acts as central storage for the structured data of its Wikimedia sister projects (Anonymous 2022, Vrandečić and Krötzsch 2014). The content of Wikidata is available under a free license, exported using standard formats, and can be interlinked to other open data sets on the linked data web including life sciences (Anonymous 2022, Mitraka et al. 2015).The study will use existing IndExs services for the 1,300 IndExs editors and 2,200 disambiguated exsiccata series and expand them for linked data / semantic web approaches. It will explore the usability of Wikidata:for disambiguation of person names (=editors) in IndExs by adapting Wikidata Identifiers,for adding information to existing Wikidata person Q-entities (items) via statements on persons´ work,for integrating IndExs information in Wikidata with URI for "IndExs Exsiccata ID" via a newly proposed P-entity (property) for this special kind of creative work in natural science,for adding new Q-entities in Wikidata for IndExs editors.These editors of published booklet series with distributed physical material fulfill the Wikidata criteria of notability. They are often more or less well-known botanists and mycologists. By their published work they might even more fulfill the criteria than certain persons categorized as botanical collectors with assignment of a Wikidata ID through activities of CETAF, DiSSCo and COST MOBILISE.
4
Zhao, Qing, Chengkui Zhang, Hao Li, Tingting Zhao, Chenzhou Cui, and Dongwei Fan. "TT-HEALpix: A New Data Indexing Strategy for Efficient Cross-match of Large-scale Astronomical Catalogs." Publications of the Astronomical Society of the Pacific 136, no. 3 (2024): 034501. http://dx.doi.org/10.1088/1538-3873/ad2721.
Full textAbstract:
Abstract Cross-matching is an indispensable operation in the data preparation, analysis, and research processes of multi-band astronomy and time-domain astronomy. Multi-catalog time-series data reconstruction is an important part of time-domain astronomy. In the large-scale distributed reconstruction process, boundary problems have always affected the accuracy of time-series data. To optimize these boundary problems and improve data precision, this paper proposes a new hybrid astronomical data indexing method called Translated Transformation based HEALPix Dual Index (TT-HEALPix). Under the reasonable Healpix division level, by translation transformation, the two indexes before and after the transformation form a unique pseudo-hybrid index strategy, which not only retains the advantages of the hybrid index scheme suitable for large-scale parallel computing, but also compensates for its shortage of high omission at the block boundary position. Based on TT-HEALPix, this paper completes the multi-catalog time-series reconstruction process on the Spark platform and compares it with the HEALPix+HTM hybrid indexing strategy. The experiments demonstrate that TT-HEALPix has significant advantages over the traditional HEALPix+HTM hybrid indexing method in terms of data accuracy and cross-matching efficiency. At level 9 of the Healpix index, TT-HEALPix achieves a 6%–19% improvement in cross-matching efficiency in a distributed environment compared to HEALPix+HTM. In terms of data accuracy, for the AST3-II dataset at level 9, TT-HEALPix has 62.2% accuracy improvement over HEALPix and 45.5% improvement over HEALPix+HTM. In conclusion, the proposed novel indexing strategy, TT-HEALPix, is better suited to the efficiency and accuracy requirements of cross-match.
5
Allen, David E., and Michael McAleer. "A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of West Texas Intermediate Oil Prices and the DOW JONES Index." Energies 13, no. 15 (2020): 4011. http://dx.doi.org/10.3390/en13154011.
Full textAbstract:
The paper features an examination of the link between the behaviour of oil prices and DowJones Index in a nonlinear autoregressive distributed lag nonlinear autoregressive distributed lag (NARDL) framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a monthly West Texas Intermediate (WTI) crude oil series from Federal Reserve Bank of St Louis (FRED), commencing in January 2000 and terminating in February 2019, and a corresponding monthly DOW JONES index adjusted-price series obtained from Yahoo Finance. Both series are adjusted for monthly USA CPI values to create real series. The results of the analysis suggest that movements in the lagged real levels of monthly WTI crude oil prices have very significant effects on the behaviour of the DOW JONES Index. They also suggest that negative movements have larger impacts than positive movements in WTI prices, and that long-term multiplier effects take about 9 to 12 months to take effect.
6
Lisdawami, Indi Masita. "PENGARUH KURS, INFLASI DAN INDEKS PRODUKSI INDUSTRI TERHADAP JAKARTA ISLAMIC INDEX PERIODE 2010-2019." Ulumuna: Jurnal Studi Keislaman 7, no. 1 (2021): 90–114. http://dx.doi.org/10.36420/ju.v7i1.4925.
Full textAbstract:
This study aims to analyze the effect of macroeconomic factors on the Jakarta Islamic Index (JII). The variables in this study are exchange rate, inflation, and Industrial Production Index (IPI). This study uses the Autoregressive Distributed (ARDL) model. The data used in this study is secondary data in the form of monthly time series (time series) from January 2010 to December 2019 taken from publications from the Central Statistics Agency, Bank Indonesia, related journals and references. The results of this study are the exchange rate and inflation variables have a negative and significant effect on JII in the short and long term. Industrial Production Index (IPI) has no significant effect.
7
Wang, Nianyong, Muhammad Haroon Shah, Kishwar Ali, Shah Abbas, and Sami Ullah. "Financial Structure, Misery Index, and Economic Growth: Time Series Empirics from Pakistan." Journal of Risk and Financial Management 12, no. 2 (2019): 100. http://dx.doi.org/10.3390/jrfm12020100.
Full textAbstract:
This study empirically analyzes the impact of the financial structure and misery index on economic growth in Pakistan. We adopted Autoregressive-Distributed Lag (ARDL) for a co-integration approach to the data analysis and used time series data from 1989 to 2017. We used GDP as the dependent variable; the Financial Development index (FDI) and misery index as the explanatory variables; and remittances, real interest, and trade openness as the control variables. The empirical results indicate the existence of a long-term relationship among the included variables in the model and the FD index, misery index, interest rate, trade openness, and remittances as the main affecting variables of GDP in the long run. The government needs appropriate reform in the financial sector and external sector in order to achieve a desirable level of economic growth in Pakistan. The misery index is constructed based on unemployment and inflation, which has a negative implication on the economic growth, and the government needs policies to reduce unemployment and inflation.
8
Allen, David, and Michael McAleer. "A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes." Risks 9, no. 11 (2021): 195. http://dx.doi.org/10.3390/risks9110195.
Full textAbstract:
The paper features an examination of the link between the behaviour of the FTSE 100 and S&P500 Indexes in both an autoregressive distributed lag ARDL, plus a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a daily FTSE adjusted price series, commencing in April 2009 and terminating in March 2021, and a corresponding daily S&P500 Index adjusted-price series obtained from Yahoo Finance. The data period includes all the gyrations caused by the Brexit vote in the UK, beginning with the vote to leave in 2016 and culminating in the actual agreement to withdraw in January 2020. It was then followed by the impact of the global spread of COVID-19 from the beginning of 2020. The results of the analysis suggest that movements in the contemporaneous levels of daily S&P500 Index levels have very significant effects on the behaviour of the levels of the daily FTSE 100 Index. They also suggest that negative movements have larger impacts than do positive movements in S&P500 levels, and that long-term multiplier impacts take about 10 days to take effect. These effects are supported by the results of quantile regression analysis. A key result is that weak form market efficiency does not apply in the second period.
9
NASREEN, SAMIA, and SOFIA ANWAR. "FINANCIAL STABILITY AND THE ROLE OF ECONOMIC AND FINANCIAL INTEGRATION IN SOUTH ASIA: EVIDENCE FROM TIME-SERIES DATA." Singapore Economic Review 65, no. 02 (2017): 303–33. http://dx.doi.org/10.1142/s0217590817500011.
Full textAbstract:
Using the aggregate financial stability index (AFSI) which measures the gradual progression and changes in financial market stability, this paper empirically evaluates the impact of financial and economic integration on financial stability in South Asian countries using time-series data for the period 1980–2012. Auto-regressive distributed lag (ARDL) Bounds testing approach to cointegration is applied to ensure long-run relationship between variables. Bound F-test results confirm the long-run relationship between selected variables. The estimated results show that economic and financial integration has exerted a significant negative effect on financial stability in long run.
10
Sagajoka, Estherlina. "Analisis Wilayah Kecamatan Potensial Melalui Pendekatan Tipologi Klassen di Kabupaten Ende." Analisis 19, no. 1 (2020): 39–49. http://dx.doi.org/10.37478/analisis.v19i1.322.
Full textAbstract:
This study aims to determine the comparison of the results of the inequality analysis of economic development between districts / cities in the province of East Nusa Tenggara for the period of 2013-2018. The method used in this research is quantitative descriptive analysis using the Williamson index, and Theil Entropy Index, using time data per capita PDRB series and population data for each district / city in 2013-2018.
 The Williamson Index analysis results show that the economic development sector inequality in 21 districts in NTT province is very evenly distributed (low inequality) except for the city of Kupang, which has an Williamson Index value of 1.49 other than districts in NTT province in the period 2013-2018. The Intra Index Analysis Results show spatial inequality within the regency. The city of Nusa Tenggara Timur province is fairly evenly distributed within the regency except the city of Kupang shows an unequal inequality compared to 21 other districts. Through the Theil Entropy Index calculation of development inequality between 21 regencies and Kupang tend to widen (divergence) which has Theil Index of 798,15, while the other 21 districts in the 2013-2018 period have the Theil Entropy Index Index 211,26 for Regencies and TTS 201,11, while other districts have an index numberbelow 200.
11
Liu, Xuze, Yuhai Zhao, Tongze Xu, Fazal Wahab, Yiming Sun, and Chen Chen. "Efficient False Positive Control Algorithms in Big Data Mining." Applied Sciences 13, no. 8 (2023): 5006. http://dx.doi.org/10.3390/app13085006.
Full textAbstract:
The typical hypothesis testing issue in statistical analysis is determining whether a pattern is significantly associated with a specific class label. This usually leads to highly challenging multiple-hypothesis testing problems in big data mining scenarios, as millions or billions of hypothesis tests in large-scale exploratory data analysis can result in a large number of false positive results. The permutation testing-based FWER control method (PFWER) is theoretically effective in dealing with multiple hypothesis testing issues. In reality, however, this theoretical approach confronts a serious computational efficiency problem. It takes an extremely long time to compute an appropriate FWER false positive control threshold using PFWER, which is almost impossible to achieve in a reasonable amount of time using human effort on medium- or large-scale data. Although some methods for improving the efficiency of the FWER false positive control threshold calculation have been proposed, most of them are stand-alone, and there is still a lot of space for efficiency improvement. To address this problem, this paper proposes a distributed PFWER false-positive threshold calculation method for large-scale data. The computational effectiveness increases significantly when compared to the current approaches. The FP-growth algorithm is used first for pattern mining, and the mining process reduces the computation of invalid patterns by using pruning operations and index optimization for merging patterns with index transactions. The distributed computing technique is introduced on this basis, and the constructed FP tree is decomposed into a set of subtrees, each corresponding to a subtask. All subtrees (subtasks) are distributed to different computing nodes. Each node independently calculates the local significance threshold according to the designated subtasks. Finally, all local results are aggregated to compute the FWER false positive control threshold, which is completely consistent with the theoretical result. A series of experimental findings on 11 real-world datasets demonstrate that the distributed algorithm proposed in this paper can significantly improve the computation efficiency of PFWER while ensuring its theoretical accuracy.
12
López-Herrera, Francisco, Alejandra Cabello, and Edgar Ortiz. "Economic activity and financial variables in Mexico." PANORAMA ECONÓMICO 14, no. 28 (2019): 9. http://dx.doi.org/10.29201/pe-ipn.v14i27.215.
Full textAbstract:
This paper analyzes the relationship between economic activity in Mexico and a set of relevant Mexican financial variables. Monthly data for the period January 1993 to August 2018 includes time series of global economic activity index, consumer price index, peso-USD exchange rate, international reserves, interest rate of short-term Mexican T-bills, Mexico´s stock market index and its level of activity as measured by the volume of operations on variable income assets. The analysis is based on an Autoregressive Distributed Lag (ADRL) model. The empirical evidence reveals that all explanatory variables, except the stock market index, show a long-run relationship with the level of Mexican economy activity.
13
López-Herrera, Francisco, Alejandra Cabello, and Edgar Ortiz. "Economic activity and financial variables in Mexico." Panorama Económico 14, no. 28 (2019): 7–32. http://dx.doi.org/10.29201/peipn.v14i28.40.
Full textAbstract:
This paper analyzes the relationship between economic activity in Mexico and a set of relevant Mexican financial variables. Monthly data for the period January 1993 to August 2018 includes time series of global economic activity index, consumer price index, peso-USD exchange rate, international reserves, interest rate of short-term Mexican T-bills, Mexico´s stock market index and its level of activity as measured by the volume of operations on variable income assets. The analysis is based on an Autoregressive Distributed Lag (ADRL) model. The empirical evidence reveals that all explanatory variables, except the stock market index, show a long-run relationship with the level of Mexican economy activity.
14
Krakauer, Jesse C., and Nir Y. Krakauer. "Combining Body Mass and Shape Indices in Clinical Practice." Case Reports in Medicine 2016 (2016): 1–4. http://dx.doi.org/10.1155/2016/1526175.
Full textAbstract:
We present preliminary clinical experience with combined consideration of the commonly used BMI (body mass index) and the newly developed ABSI (a body shape index) using a point of care anthropometric calculator for comparisons of index values and associated relative risks to population normals. In a series of 282 patients, BMI and ABSI were close to being independently distributed, supporting the value of considering both indices. Three selected cases illustrate scenarios where assessment of ABSI together with BMI could inform patient care and counseling. These data suggest that combined assessment of BMI and ABSI may prove useful in clinical practice.
15
Chu, Patrick K. K. "Study on the Non-Random and Chaotic Behavior of Chinese Equities Market." Review of Pacific Basin Financial Markets and Policies 06, no. 02 (2003): 199–222. http://dx.doi.org/10.1142/s0219091503001055.
Full textAbstract:
After the stock market crash of October 19, 1987, interest in nonlinear dynamics and chaotic dynamics have increased in the field of financial analysis. The extent that the daily return data from the Shanghai Stock Exchange Index and the Shenzhen Stock Exchange Index exhibit non-random, nonlinear and chaotic characteristics are investigated by employing various tests from chaos theory. The Hurst exponent in R/S analysis rejects the hypothesis that the index return series are random, independent and identically distributed. The BDS test provides evidence for nonlinearity. The estimated correlation dimensions provide evidence for deterministic chaotic behaviors.
16
Zhao, Fanfei, Guanqun Zhang, Zhaoli Wang, and Xiangyu Hao. "Construction of Higher Education Management Data Analysis Model Based on Association Rules." Scientific Programming 2022 (April 23, 2022): 1–12. http://dx.doi.org/10.1155/2022/5414238.
Full textAbstract:
This paper adopts the analytical method of intelligent programming to conduct in-depth research and analysis on university education management and designs a corresponding university education management model to be applied in actual teaching. Based on the basic theories and methods of big data distributed data mining and machine learning, automated machine learning, and big data programming computational methods, this paper combines the importance and technical challenges of the algorithms themselves and the background of the practical application needs of the industry and firstly selects a series of data mining and machine learning algorithms that are commonly used based on high complexity, outstanding computational efficiency problems, and difficult to design distributed algorithms. The research on efficient large-scale distributed parallelized data mining and machine learning methods and algorithms is carried out. As an innovative teaching model, the experiential teaching model focuses on the cultivation of individual students’ independent learning ability and subjective initiative, which not only can effectively activate the classroom atmosphere and improve the teaching effect but also meet the requirements of classroom teaching for the development of the times. The empirical evidence of the experiential teaching model proposed in this paper is carried out, and the implementation process of the experiential teaching model of high school physics is further improved. In terms of the overall implementation effect, the experiential teaching model proposed in the paper can deepen students’ understanding of concepts and physical laws, thus enabling them to solve physics problems better, and has a positive and active effect on enhancing students’ learning interests and attitudes. In the case of system security, the average comprehensive index of each unit is 61.11, 22 are higher than the average index, accounting for 61.11%, 14 are lower than the average index, accounting for 38.89%, and 23 management departments’ management systems passed the safety monitoring, accounting for 63.89%. At the same time, the proposed model also provides educational practitioners with a more reliable framework for instructional design, which has realistic reference value and significance.
17
Fatoni, Ahmad. "Pengaruh Ketidakpastian Ekonomi Terhadap Stabilitas Perbankan Syariah Di Indonesia." Jurnal Ilmiah Ekonomi Islam 8, no. 3 (2022): 2903. http://dx.doi.org/10.29040/jiei.v8i3.5385.
Full textAbstract:
This study aims to analyze the influence of economic uncertainty on the stability of Islamic banking in Indonesia. The study used time series data with the Auto Regresive Distributed Lag (ARDL) approach. The sample used is the entire Islamic Commercial Bank from March 2010 to September 2021. The results showed that the World Uncertainty Index (WUI) and the Economic Policy Uncertainty Index which are measures of economic uncertainty have a significant influence on the stability of Islamic banking in Indonesia. Although the direction of the relationship between the two has differences.
18
Herbst, M., M. C. Casper, J. Grundmann, and O. Buchholz. "Comparative analysis of model behaviour for flood prediction purposes using Self-Organizing Maps." Natural Hazards and Earth System Sciences 9, no. 2 (2009): 373–92. http://dx.doi.org/10.5194/nhess-9-373-2009.
Full textAbstract:
Abstract. Distributed watershed models constitute a key component in flood forecasting systems. It is widely recognized that models because of their structural differences have varying capabilities of capturing different aspects of the system behaviour equally well. Of course, this also applies to the reproduction of peak discharges by a simulation model which is of particular interest regarding the flood forecasting problem. In our study we use a Self-Organizing Map (SOM) in combination with index measures which are derived from the flow duration curve in order to examine the conditions under which three different distributed watershed models are capable of reproducing flood events present in the calibration data. These indices are specifically conceptualized to extract data on the peak discharge characteristics of model output time series which are obtained from Monte-Carlo simulations with the distributed watershed models NASIM, LARSIM and WaSIM-ETH. The SOM helps to analyze this data by producing a discretized mapping of their distribution in the index space onto a two dimensional plane such that their pattern and consequently the patterns of model behaviour can be conveyed in a comprehensive manner. It is demonstrated how the SOM provides useful information about details of model behaviour and also helps identifying the model parameters that are relevant for the reproduction of peak discharges and thus for flood prediction problems. It is further shown how the SOM can be used to identify those parameter sets from among the Monte-Carlo data that most closely approximate the peak discharges of a measured time series. The results represent the characteristics of the observed time series with partially superior accuracy than the reference simulation obtained by implementing a simple calibration strategy using the global optimization algorithm SCE-UA. The most prominent advantage of using SOM in the context of model analysis is that it allows to comparatively evaluating the data from two or more models. Our results highlight the individuality of the model realizations in terms of the index measures and shed a critical light on the use and implementation of simple and yet too rigorous calibration strategies.
19
Huang, Xiaohui, Jiabao Li, Jining Yan, and Lizhe Wang. "An adaptive geographic meshing and coding method for remote sensing data." IOP Conference Series: Earth and Environmental Science 1004, no. 1 (2022): 012006. http://dx.doi.org/10.1088/1755-1315/1004/1/012006.
Full textAbstract:
Abstract Spatial indexing techniques, inherently data structures, are generally used in portals opened by institutions or organizations to efficiently filter RS images according to their spatial extent, thus providing researchers with fast Remote Sensing (RS) image data discovery ability. Specifically, space-based spatial indexing approaches are widely adopted to index RS images in distributed environments by mapping RS images in two-dimensional space into several one-dimensional spatial codes. However, current spatial indexing approaches still suffer from the boundary objects problem, which leads to multiple spatial codes for a boundary-crossing RS image and thus alleviates the performance of spatial indexes built on top of these spatial codes. To solve this problem, we propose an adaptive geographic meshing and coding method (AGMD) by combining the famous subdivision model GeoSOT and XZ-ordering to generate only one spatial code for RS images with different spatial widths. Then, we implement our proposed method with a unified big data programming model, (i.e., Apache Beam), to enable its execution in various distributed computing engines (e.g., MapReduce, and Apache Spark, etc.) in distributed environments. Finally, we conduct a series of experiments on real datasets, the archived Landsat metadata collection in level 2. The results show that the proposed AGMD method performs well on metrics, including the following aspects: the effectiveness of the storage overhead and the time cost are up to 359.7% and 58.02 %, respectively.
20
Firdous, Iram, Arshad Mahmood, Muhammad Abdul Rahman, and Farida Faisal. "Monetary Condition Index: Empirical Evidence from Pakistan." iRASD Journal of Economics 5, no. 2 (2023): 532–45. http://dx.doi.org/10.52131/joe.2023.0502.0144.
Full textAbstract:
The study focuses on assessing the Monetary Condition Index (MCI) in Pakistan, which measures the tightness or easiness of the country's monetary policy. It analyzes the impacts of interest rates and exchange rates on inflation and GDP growth, using time series data from 1975 to 2020. Monetary Condition Index is calculated by using time series data, employing annual data set from 1975 to 2020. Two different methods are applied to find the weights of interest rate and exchange rate, the one is the Principal Component Analysis and the other is co-integration method employing the Autoregressive Distributed Lag model. The research aims to provide valuable insights for policymakers, planners, and economists to optimize monetary policies and understand the transmission mechanism in the economy. The study also explores the historical impacts of interest rates and exchange rates, with potential implications for other economies. The Monetary Condition Index is introduced as a useful policy locator and an indicator of the monetary policy stance. Additionally, the research seeks to validate the MCI's reliability as a policy indicator and address certain research gaps related to its effectiveness, dynamics, and generalizability. The study concluded that the MCI is sensitive to variations, and its calculation and methodology are essential. The monetary stance in Pakistan remained tight from 2017 to 2020, representing the highest trend in history. However, the effectiveness of using interest rate tools to control inflation in Pakistan was questioned, suggesting that changes in monetary policy tools might be necessary to achieve the objectives of the State Bank of Pakistan in line with Islamic economics principles.
21
Karantzalos, K., A. Karmas, and A. Tzotsos. "Monitoring crop growth and key agronomic parameters through multitemporal observations and time series analysis from remote sensing big data." Advances in Animal Biosciences 8, no. 2 (2017): 394–99. http://dx.doi.org/10.1017/s2040470017001261.
Full textAbstract:
In this paper, novel geospatial services are presented which are able to process on the server-side numerous remote sensing data based on big data frameworks like Hadoop and Rasdaman. The developed system itself features several software modules that orchestrate the different image processing algorithms responsible for the production of consistent value-added maps like canopy greenness and leaf area index. Through distributed multitemporal analysis, the entire crop growth cycle can be continuously monitored through the analysis of time-series observations. These observations cover multiple crop growth cycles, offering invaluable information by linking weather statistical data with the start, the end and the duration of each growth cycle enabling critical decisions by direct comparison with the current crop growth state.
22
Das, Divyaanshu. "Stock Market Indices Direction Prediction: Time Series, Macro Economic Factors and Distributed Lag Models." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 2638–44. http://dx.doi.org/10.22214/ijraset.2022.41733.
Full textAbstract:
Abstract: A number of studies have been conducted to model the stock market indices using pure time series models or regression models based on macro economic variables. In this study, instead of focusing on modeling the actual levels of stock market indices I focus on predicting the direction (up/down) as investors who rely on technical analysis are more interested in the direction of stock market index than the actual prediction value. Therefore, in this study I look at best modelling approach for the direction prediction: time series (ARMA) or macro factor models or combination of both (ARDL). My study shows that macro factor models outperform for direction prediction as compared to ARMA or ARDL models. The study was performed on stock market direction prediction of stock indices of three South Asia countries: India, Pakistan and Malaysia. The macro economic factors that are considered for direction prediction are: Inflation, Unemployment and Exchange Rate monthly data from March 2016 to September 2021. Keywords: stock, equity, prediction, models, direction, ARMA, ARDL,macro
23
Barbulescu, Alina, Lucica Barbes, and Cristian Stefan Dumitriu. "Assessing the Water Pollution of the Brahmaputra River Using Water Quality Indexes." Toxics 9, no. 11 (2021): 297. http://dx.doi.org/10.3390/toxics9110297.
Full textAbstract:
Water quality is continuously affected by anthropogenic and environmental conditions. A significant issue of the Indian rivers is the massive water pollution, leading to the spreading of different diseases due to its daily use. Therefore, this study investigates three aspects. The first one is testing the hypothesis of the existence of a monotonic trend of the series of eight water parameters of the Brahmaputra River recorded for 17 years at ten hydrological stations. When this hypothesis was rejected, a loess trend was fitted. The second aspect is to assess the water quality using three indicators (WQI)–CCME WQI, British Colombia, and a weighted index. The third aspect is to group the years and the stations in clusters used to determine the regional (spatial) and temporal trend of the WQI series, utilizing a new algorithm. A statistical analysis does not reject the hypothesis of a monotonic trend presence for the spatially distributed data but not for the temporal ones. Hierarchical clustering based on the computed WQIs detected two clusters for the spatially distributed data and two for the temporal-distributed data. The procedure proposed for determining the WQI temporal and regional evolution provided good results in terms of mean absolute error, root mean squared error (RMSE), and mean absolute percentage error (MAPE).
24
Kulu, Evans, Samuel Mensah, and Prince Mike Sena. "Effects of foreign direct investment on economic growth in Ghana: the role of institutions." Economics of Development 20, no. 1 (2021): 23–34. http://dx.doi.org/10.21511/ed.20(1).2021.03.
Full textAbstract:
The role of institutions in both the inflow and the impact of foreign direct investment is of great im¬portance. The quality of institutions in a country can direct investment towards improving growth. This paper analyzes the individual and combined effect of foreign direct investment and institutions on economic growth in Ghana. The paper used the Auto Regressive Distributed Lag (ARDL) tech¬nique for secondary data obtained from 1995 to 2019. All data series, except for the quality institution index, were drawn from the World Bank Development Indicators. Institutional Quality Index data was obtained from the Heritage Foundation’s Economic Freedom Index website. The results of the ARDL model indicate that foreign direct investment and a quality institutional index together have a significantly positive effect on a country’s economic growth compared to their individual effects in both the short and long run. The study recommends that government policies should be aimed at attracting foreign direct investment while strengthening institutions and regulations to enhance output growth.
25
Orman, Turgut, and İlkay Dellal. "Cointegration Analysis of Exchange Rate Volatility and Agricultural Exports in Turkey: an Ardl Approch." Turkish Journal of Agriculture - Food Science and Technology 9, no. 6 (2021): 1180–85. http://dx.doi.org/10.24925/turjaf.v9i6.1180-1185.4456.
Full textAbstract:
This study aims to reveal the impact of exchange rate volatility on agricultural exports of Turkey by using the Autoregressive Distributed Lag Model. While quarterly time series data covering period of 2001: Q1 to 2018: Q4 were used to carry out analyses, Exponential Generalized Autoregressive Conditional Heteroscedasticity (1.1) is used to acquire exchange rate volatility series. The research findings showed that agricultural export is cointegrated with exchange rate volatility, producer price index and real effective exchange rate. Furthermore, our findings indicate that increases in real effective exchange rate have a statistically significant positive influence on the export volume whereas exchange rate volatility has negative impact on it.
26
Sumantri, Muhammad Bayu Aji. "Analysing the Impact of Global Commodity Prices on the IDX Energy Index." Prosiding Seminar Nasional Forum Manajemen Indonesia - e-ISSN 3026-4499 2 (August 30, 2024): 98–106. http://dx.doi.org/10.47747/snfmi.v2i1.2014.
Full textAbstract:
This study aims to analyse the influence of global commodity prices on the energy index prices in Indonesia (IDXENERGY). As proxy of the research this research uses world crude oil prices, world gold prices, and world natural gas prices as the variable of global commodities. The research utilizes monthly time series data from January 2021 to December 2023. The ARDL (Autoregressive Distributed Lag) method is employed to model the short-term and long-term relationships between two or more variables. The study finds that, in the short term, world crude oil prices and world gold prices significantly affect the IDXENERGY index, while the world natural gas price variable does not have a significant impact. In the long-term analysis, no variable is found to have a significant influence on the IDXENERGY index.
27
Sumantri, Muhammad Bayu Aji. "Analysing the Impact of Global Commodity Prices on the IDX Energy Index." Proceedings of Forum Manajemen Indonesia's International Conferences, no. 1 (August 30, 2024): 98–106. https://doi.org/10.47747/fmiic.v1i1.2203.
Full textAbstract:
This study aims to analyse the influence of global commodity prices on the energy index prices in Indonesia (IDXENERGY). As proxy of the research this research uses world crude oil prices, world gold prices, and world natural gas prices as the variable of global commodities. The research utilizes monthly time series data from January 2021 to December 2023. The ARDL (Autoregressive Distributed Lag) method is employed to model the short-term and long-term relationships between two or more variables. The study finds that, in the short term, world crude oil prices and world gold prices significantly affect the IDXENERGY index, while the world natural gas price variable does not have a significant impact. In the long-term analysis, no variable is found to have a significant influence on the IDXENERGY index.
28
Elboiashi, Hosein, and Abdelhakim Embaya. "Economic Corruption and its Impact on Achieving the Sustainable Development Goals (SDGs) in Libya." International Journal of Financial, Administrative, and Economic Sciences 4, no. 2 (2025): 248–66. https://doi.org/10.59992/ijfaes.2025.v4n2p9.
Full textAbstract:
This paper investigates the relationship between economic corruption and the achievement of Sustainable Development Goals (SDGs) in Libya. Utilizing time-series data from 1990 to 2019 and employing an Autoregressive Distributed Lag (ARDL) model, the paper assesses the impact of economic corruption, measured by the International Country Risk Guide (ICRG) index, on the Sustainable Development Index (SDI). The findings reveal a significant negative correlation between economic corruption and sustainable development. The paper further examines the roles of economic growth, human capital, inflation, and unemployment as key determinants influencing sustainable development outcomes. The results emphasize the need for comprehensive strategies to combat corruption, promote good governance, and foster inclusive economic development to accelerate progress toward the SDGs in Libya.
29
Cembawan Wijaksana, Arya. "ANALISIS PENGARUH PRODUK DOMESTIK REGIONAL BRUTO, INDEKS PEMBANGUNAN MANUSIA, DAN TINGKAT PENGANGGURAN TERBUKA TERHADAP TINGKAT KEMISKINAN DI PROVINSI BANTEN PERIODE TAHUN 2016-2021." Profit: Jurnal Manajemen, Bisnis dan Akuntansi 1, no. 4 (2022): 99–113. http://dx.doi.org/10.58192/profit.v1i4.250.
Full textAbstract:
The poverty rate in Banten Province is still very high so that the level of welfare in society is still low and not evenly distributed, this is because many factors cause poverty such as Regional Gross Domestic Product, Human Development Index, and Open Unemployment Rate. This research was conducted with the aim of testing and analyzing the effect of the Gross Regional Domestic Product, the Human Development Index, and the Open Unemployment Rate on the Poverty Rate in Banten Province in the 2016-2021 period. This research is quantitative research. Using secondary data types obtained from BPS Banten Province and other supporting journal literature. In this study the method used was panel data method in the form of cross sections of 8 regencies/cities in Banten Province and time series data from 2016-2021 using the Eviews 9 program.
30
Parmentier, Frans-Jan W., Lennart Nilsen, Hans Tømmervik, and Elisabeth J. Cooper. "A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales." Earth System Science Data 13, no. 7 (2021): 3593–606. http://dx.doi.org/10.5194/essd-13-3593-2021.
Full textAbstract:
Abstract. Near-surface remote sensing techniques are essential monitoring tools to provide spatial and temporal resolutions beyond the capabilities of orbital methods. This high level of detail is especially helpful to monitor specific plant communities and to accurately time the phenological stages of vegetation – which satellites can miss by days or weeks in frequently clouded areas such as the Arctic. In this paper, we describe a measurement network that is distributed across varying plant communities in the high Arctic valley of Adventdalen on the Svalbard archipelago with the aim of monitoring vegetation phenology. The network consists of 10 racks equipped with sensors that measure NDVI (normalized difference vegetation index), soil temperature, and moisture as well as time-lapse RGB cameras (i.e. phenocams). Three additional time-lapse cameras are placed on nearby mountains to provide an overview of the valley. We derived the vegetation index GCC (green chromatic channel) from these RGB photos, which has similar applications as NDVI but at a fraction of the cost of NDVI imaging sensors. To create a robust time series for GCC, each set of photos was adjusted for unwanted movement of the camera with a stabilizing algorithm that enhances the spatial precision of these measurements. This code is available at https://doi.org/10.5281/zenodo.4554937 (Parmentier, 2021) and can be applied to time series obtained with other time-lapse cameras. This paper presents an overview of the data collection and processing and an overview of the dataset that is available at https://doi.org/10.21343/kbpq-xb91 (Nilsen et al., 2021). In addition, we provide some examples of how these data can be used to monitor different vegetation communities in the landscape.
31
Sharma, Shiv Kumar, and Dr Jutimala Bora. "Indirect Tax Reform And Market Efficiency: An Empirical Study of Indian Stock Market." Review of Applied Socio-Economic Research 27, no. 1 (2024): 18–25. http://dx.doi.org/10.54609/reaser.v27i1.257.
Full textAbstract:
The biggest tax reform of the Indian Government in this present decade is the Goods and Services Tax (GST), which was passed in parliament on 8th September 2016 and was enforced from 1st July 2017. Eight years of data from the Bombay Stock Exchange Sensex and the National Stock Exchange Nifty50 index is studied. The data is divided into two sub-periods before GST was passed from parliament period - I and after GST was passed from the parliament of India period - II. The study intends to test the market efficiency in its weak form. To check the time series data for normality, stationarity, random walk, and autocorrelation function, Kolmogorov-Smirnov (KS) test, Augmented Dickey-Fuller (ADF) test, Phillips Perron (PP) test is used for the analyse of the time-series data. The study found that returns are not normally distributed and the market is inefficient in a weak form for both periods under study.
32
Fuentes, Daniel, and Nicola Fiore. "The LifeWatch approach to the exploration of distributed species information." ZooKeys 463 (December 12, 2014): 133–48. https://doi.org/10.3897/zookeys.463.8397.
Full textAbstract:
This paper introduces a new method of automatically extracting, integrating and presenting information regarding species from the most relevant online taxonomic resources. First, the information is extracted and joined using data wrappers and integration solutions. Then, an analytical tool is used to provide a visual representation of the data. The information is then integrated into a user friendly content management system. The proposal has been implemented using data from the Global Biodiversity Information Facility (GBIF), the Catalogue of Life (CoL), the World Register of Marine Species (WoRMS), the Integrated Taxonomic Information System (ITIS) and the Global Names Index (GNI). The approach improves data quality, avoiding taxonomic and nomenclature errors whilst increasing the availability and accessibility of the information.
33
Sun, Tieshuang. "Research on Financial Market Risk Based on GARCH-M Model." E3S Web of Conferences 251 (2021): 01106. http://dx.doi.org/10.1051/e3sconf/202125101106.
Full textAbstract:
Since 1970, with the gradual acceleration of economic globalization and the rapid development of information technology, the financial market has become increasingly unstable. Therefore, we must enhance our competitiveness in the financial market, enhance our ability to resist risks, and master effective measures such as measuring risks. In this paper, GARCH-M model and VAR method are used to study the value at risk of financial market and make an empirical analysis. Firstly, the VAR value calculation method based on GARCH-M model under generalized error distribution is given. Secondly, the closing price of Shanghai Stock Exchange Index is selected as sample data, and Eviews software is used to analyze its characteristics. The results show that the logarithmic yield series of the closing price of Shanghai Stock Exchange Index is not normally distributed, and the series has fluctuation aggregation effect, autocorrelation effect and heteroscedasticity effect. Finally, GARCH-M model is established, and VAR estimates at 95% and 99% confidence levels are calculated and tested. The results show that GARCH-M(1,1) model is more suitable for estimating the risk of logarithmic return rate of closing price of Shanghai Composite Index.
34
Simeon, Ekpete, Marshall, and Kenn-Ndubuisi, Juliet Ifechi*. "Macroeconomic Effects on Stock Market Returns in Nigeria." Noble International Journal of Economics and Financial Research, no. 65 (November 23, 2021): 99–109. http://dx.doi.org/10.51550/nijefr.65.99.109.
Full textAbstract:
The purpose of this study is to investigate the relationship between macroeconomic effects on stock market returns in Nigeria employing the CBN annual time series data spanning from 1985-2019. The study applied unit root test, auto-regressive distributed Lag and granger causality tests to investigate the relationship between all share index and interest rate, inflation rate, exchange rate. The unit root tests results for stationarity revealed that the entire variables are reliable for economic decisions. The findings of the study revealed that interest rate was negative and not significantly related with the all share index; also inflation rate was negative and not significantly related with the all share index while exchange rate was positive and significantly related with the all share index. The granger causality result revealed Uni-directional causality which implies no causality. This study recommends that macroeconomic factors should be adequately managed by the Central Bank of Nigeria with the view to promoting investors confidence in the stock market.
35
Khoiri, Muhammad Agung, Muhammad Fidel Ganis Siregar, Sarma Nursani Lumbanraja, et al. "Testosterone and sexual function in menopausal women based on the Female Sexual Function Index (FSFI) score." Majalah Obstetri & Ginekologi 31, no. 2 (2023): 86–91. http://dx.doi.org/10.20473/mog.v31i22023.86-91.
Full textAbstract:
HIGHLIGHTS Testosterone level correlates with FSFI score, showing that sexual function correlates with hormonal physiology. FSFI is a valid and useful tool in measuring sexual function. ABSTRACT Objective: This research aimed to analyze the correlation between testosterone levels and sexual function in postmenopausal women. Materials and Methods: This research was a descriptive observational study with a case series approach conducted at Aras Kabu Health Center Outpatient Polyclinic from May to August 2022. The normality test was carried out using the Shapiro-Wilk test. If the data were not normally distributed, data would be analyzed using the Spearman correlation test. If the data were normally distributed, the data analysis would use the Pearson correlation test. Results: There was a significant relationship between Female Sexual Function Index (FSFI) score with testosterone levels and duration of menopause with p<0.05. The degree of correlation found was 0.619 between testosterone levels and FSFI scores, indicating a moderate and significant positive correlation. A correlation degree of 0.482 was found between FSFI and length of menopause which indicated a significant moderate positive correlation, while the degree of correlation between testosterone levels and length of menopause was found to be 0.711, showing a strong and significant positive correlation. Conclusion: There is a significant relationship between FSFI scores with testosterone levels and duration of menopause as well. There was also a significant relationship between testosterone levels and the duration of menopause.
36
Sezal, Levent. "DOES CURRENCY VOLATILITY AFFECT NON-PERFORMING LOANS? EVIDENCE FROM THE TURKISH BANKING SECTOR." Farabi Journal of Social Sciences 10, no. 1 (2024): 72–84. http://dx.doi.org/10.26577/fjss2024v9i2a8.
Full textAbstract:
This study aims to econometrically investigate the relationship between non-performing loans and Euro and USD indices in Turkey. The data set of the study consists of the Turkish banking sector non-performing loan ratio and Euro and American Dollar (USD) indices variables for the period 01/2003-09/2023. Time series analyses were used to test the relationship between the variables. Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests were applied to the data. Since the series became stationary at different levels, the ARDL (Autoregressive Distributed Lag Bound Test) test, one of the cointegration tests, was applied. Afterward, whether there is causality between the variables and if there is a causality relationship, the determination of the direction of causality is mutually tested with the “Granger Causality” method. According to the results of the research, a cointegration relationship was found between the non-performing loan ratios of the Turkish banking system and the Euro and USD indices. In the long run, it is concluded that a 1% increase in the Euro index is associated with a 2.93% decrease in non-performing loan ratios. On the other hand, in the short run, Euro and USD index values have no effect on non-performing loan ratios. According to the results of the Granger causality test, a unidirectional Granger causality relationship was detected between non-performing loan ratios and the Euro index. A similar unidirectional causality relationship was also found between the Euro index and the USD index.
37
Xie, Guang Long, Bu Han Zhang, Cheng Xiong Mao, and Yan Li. "Voltage Stability of Medium and Low Voltage Distribution Networks with Wind Generation Based on Hilbert-Huang Transform." Advanced Materials Research 347-353 (October 2011): 2200–2206. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.2200.
Full textAbstract:
Nowadays the voltage instability problems occur in the distribution grid due to the integration of more and more fluctuant distributed generators. This paper focuses on voltage stability of distribution networks with wind generation. A voltage stability index is presented, and calculated by an expanding Newton-Raphson power flow method in which the wind power generation nodes are modified according to the P-Q(V) character. In order to obtain the key components of fluctuant wind power, the Hilbert-Huang transform algorithm is utilized to reveal the inherent characteristics of wind power. The extrema extending method based on the mirror periodic is used in the empirical mode decomposition and can handle the end effects. According to the Hilbert spectrum and instantaneous energy of each intrinsic mode functions, the components with lower instantaneous frequency are selected to rebuild the wind power. The new rebuilt series consist of stationary and monotonic components which are smoother, and the rebuilt series can reflect the main fluctuant characteristic of the initial wind power data by comparing the index.
38
Dilmac, Murat, Serpil Sumer, and Hilal Mola. "The effect of the fear index, dollar index and bitcoin on volatility: An example from Borsa Istanbul." Asian Economic and Financial Review 13, no. 12 (2023): 970–80. http://dx.doi.org/10.55493/5002.v13i12.4902.
Full textAbstract:
The effect of the Russia–Ukraine war has fluctuated in Europe and Asia's economic conjuncture by virtue of constant shifting balances. The portfolios of investors who made decisions in uncertain conditions have been affected by these fluctuations that have caused volatility in the stock market's indexes. The aim of this study is to examine the impact of the Fear Index (FI), the Dollar Index, and Bitcoin on the volatility of the Borsa Istanbul 100 Index (BIST). Autoregressive distributed lag (ARDL) time series analysis was used for the study, which revealed that the Dollar Index has no effect on volatility, while the FI was found to have an effect on volatility both in the short and long runs. In addition, Bitcoin was determined to have an effect on volatility only in the long run. When the period of the data used is examined, the outbreak of the Russia–Ukraine war in February 2022 is thought to be the reason for the increase in the FI. It can be assumed that the decisions of investors to invest in the BIST were adversely affected by the war as a natural consequence of this, and investors who ceased investing in the BIST index opted to invest elsewhere.
39
Akanbi, Adeyinka, and Muthoni Masinde. "A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring." Sensors 20, no. 11 (2020): 3166. http://dx.doi.org/10.3390/s20113166.
Full textAbstract:
In recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical ‘batch’ processing—extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the environmental monitoring domain still utilises legacy systems, which complicates the real-time analysis of the essential data, integration with big data platforms and reliance on batch processing. Herein, as a solution, a distributed stream processing middleware framework for real-time analysis of heterogeneous environmental monitoring and management data is presented and tested on a cluster using open source technologies in a big data environment. The system ingests datasets from legacy systems and sensor data from heterogeneous automated weather systems irrespective of the data types to Apache Kafka topics using Kafka Connect APIs for processing by the Kafka streaming processing engine. The stream processing engine executes the predictive numerical models and algorithms represented in event processing (EP) languages for real-time analysis of the data streams. To prove the feasibility of the proposed framework, we implemented the system using a case study scenario of drought prediction and forecasting based on the Effective Drought Index (EDI) model. Firstly, we transform the predictive model into a form that could be executed by the streaming engine for real-time computing. Secondly, the model is applied to the ingested data streams and datasets to predict drought through persistent querying of the infinite streams to detect anomalies. As a conclusion of this study, a performance evaluation of the distributed stream processing middleware infrastructure is calculated to determine the real-time effectiveness of the framework.
40
Zeng, Linglin, Brian D. Wardlow, Shun Hu, et al. "A Novel Strategy to Reconstruct NDVI Time-Series with High Temporal Resolution from MODIS Multi-Temporal Composite Products." Remote Sensing 13, no. 7 (2021): 1397. http://dx.doi.org/10.3390/rs13071397.
Full textAbstract:
Vegetation indices (VIs) data derived from satellite imageries play a vital role in land surface vegetation and dynamic monitoring. Due to the excessive noises (e.g., cloud cover, atmospheric contamination) in daily VI data, temporal compositing methods are commonly used to produce composite data to minimize the negative influence of noise over a given compositing time interval. However, VI time series with high temporal resolution were preferred by many applications such as vegetation phenology and land change detections. This study presents a novel strategy named DAVIR-MUTCOP (DAily Vegetation Index Reconstruction based on MUlti-Temporal COmposite Products) method for normalized difference vegetation index (NDVI) time-series reconstruction with high temporal resolution. The core of the DAVIR-MUTCOP method is a combination of the advantages of both original daily and temporally composite products, and selecting more daily observations with high quality through the temporal variation of temporally corrected composite data. The DAVIR-MUTCOP method was applied to reconstruct high-quality NDVI time-series using MODIS multi-temporal products in two study areas in the continental United States (CONUS), i.e., three field experimental sites near Mead, Nebraska from 2001 to 2012 and forty-six AmeriFlux sites evenly distributed across CONUS from 2006 to 2010. In these two study areas, the DAVIR-MUTCOP method was also compared to several commonly used methods, i.e., the Harmonic Analysis of Time-Series (HANTS) method using original daily observations, Savitzky–Golay (SG) filtering using daily observations with cloud mask products as auxiliary data, and SG filtering using temporally corrected composite data. The results showed that the DAVIR-MUTCOP method significantly improved the temporal resolution of the reconstructed NDVI time series. It performed the best in reconstructing NDVI time-series across time and space (coefficient of determination (R2 = 0.93 ~ 0.94) between reconstructed NDVI and ground-observed LAI). DAVIR-MUTCOP method presented the highest robustness and accuracy with the change of the filtering parameter (R2 = 0.99 ~ 1.00, bias = 0.001, root mean square error (RMSE) = 0.020). Only MODIS data were used in this study; nevertheless, the DAVIR-MUTCOP method proposed a universal and potential way to reconstruct daily time series of other VIs or from other operational sensors, e.g., AVHRR and VIIRS.
41
Kerr, Richard F., and Matthew J. Rogers. "FINRA relaxes restrictions on pre-inception performance data." Journal of Investment Compliance 20, no. 3 (2019): 6–9. http://dx.doi.org/10.1108/joic-04-2019-0026.
Full textAbstract:
Purpose To explain the significance of a recently issued interpretive letter in which FINRA staff agreed to permit the use of pre-inception index performance data by passively managed, registered open-end investment companies. Design/methodology/approach FINRA recently issued an interpretive letter extending previously issued guidance by permitting passively managed open-end registered investment companies including separately-managed series of a business trust to use pre-inception index performance data in Institutional Communications. Findings The 2019 Letter is an important shift in how FINRA staff views PIP data in Institutional Communications by acknowledging that passively managed open-end funds should be treated in a similar manner as passively managed exchange-traded funds. This shift will be a welcome development for FINRA member firms wishing to include PIP data in marketing materials for the passively managed open-end funds they distribute. Originality/value Practical guidance from experienced investment management and broker-dealer lawyers.
42
Khalaf, Ammar. "Foreign exchange market pressure index and monetary policy in Iraq." Ekonomski anali 63, no. 219 (2018): 61–82. http://dx.doi.org/10.2298/eka1819061k.
Full textAbstract:
This paper?s aims are to adequately measure a foreign exchange market pressure index that can be used to discover pressures in the Iraqi foreign exchange market early on, and to examine the effect of monetary policy intervention in the Iraqi foreign exchange market. The modelling approach used is Autoregressive Distributed Lag (ARDL), with monthly time series data spanning 2013-2017. The index used in this paper was able to identify different periods of pressure in the Iraqi foreign exchange market. In addition, the econometric analysis found that the traditional proxies for monetary policy intervention in the foreign exchange market, such as domestic credit and money multiplier, were ineffective in the case of Iraq. The results show that the Central Bank of Iraq (CBI) relied extensively on foreign reserves to mitigate pressures in the foreign exchange market. Due to the nature of the Iraqi economy and where the main source of foreign currency is oil exports, the CBI adopted a fixed exchange rate regime to control inflationary expectations and stabilize the foreign exchange market.
43
Sarafanov, Mikhail, Eduard Kazakov, Nikolay O. Nikitin, and Anna V. Kalyuzhnaya. "A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI." Remote Sensing 12, no. 23 (2020): 3865. http://dx.doi.org/10.3390/rs12233865.
Full textAbstract:
Satellite remote sensing has now become a unique tool for continuous and predictable monitoring of geosystems at various scales, observing the dynamics of different geophysical parameters of the environment. One of the essential problems with most satellite environmental monitoring methods is their sensitivity to atmospheric conditions, in particular cloud cover, which leads to the loss of a significant part of data, especially at high latitudes, potentially reducing the quality of observation time series until it is useless. In this paper, we present a toolbox for filling gaps in remote sensing time-series data based on machine learning algorithms and spatio-temporal statistics. The first implemented procedure allows us to fill gaps based on spatial relationships between pixels, obtained from historical time-series. Then, the second procedure is dedicated to filling the remaining gaps based on the temporal dynamics of each pixel value. The algorithm was tested and verified on Sentinel-3 SLSTR and Terra MODIS land surface temperature data and under different geographical and seasonal conditions. As a result of validation, it was found that in most cases the error did not exceed 1 °C. The algorithm was also verified for gaps restoration in Terra MODIS derived normalized difference vegetation index and land surface broadband albedo datasets. The software implementation is Python-based and distributed under conditions of GNU GPL 3 license via public repository.
44
Sun, Zhe, Juhua Luo, Jingzhicheng Yang, et al. "Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine." Remote Sensing 12, no. 18 (2020): 3086. http://dx.doi.org/10.3390/rs12183086.
Full textAbstract:
Global rapid expansion of the coastal aquaculture industry has made great contributions to enhance food security, but has also caused a series of ecological and environmental issues. Sustainable management of coastal areas requires the explicit and efficient mapping of the spatial distribution of aquaculture ponds. In this study, a Google Earth Engine (GEE) application was developed for mapping coastal aquaculture ponds at a national scale with a novel classification scheme using Sentinel-1 time series data. Relevant indices used in the classification mainly include the water index, texture, and geometric metrics derived from radar backscatter, which were then used to segment and classify aquaculture ponds. Using this approach, we classified aquaculture ponds for the full extent of the coastal area in Vietnam with an overall accuracy of 90.16% (based on independent sample evaluation). The approach, enabling wall-to-wall mapping and area estimation, is essential to the efficient monitoring and management of aquaculture ponds. The classification results showed that aquaculture ponds are widely distributed in Vietnam’s coastal area and are concentrated in the Mekong River Delta and Red River delta (85.14% of the total area), which are facing the increasing collective risk of climate change (e.g., sea level rise and salinity intrusion). Further investigation of the classification results also provides significant insights into the stability and deliverability of the approach. The water index derived from annual median radar backscatter intensity was determined to be efficient at mapping water bodies, likely due to its strong response to water bodies regardless of weather. The geometric metrics considering the spatial variation of radar backscatter patterns were effective at distinguishing aquaculture ponds from other water bodies. The primary use of GEE in this approach makes it replicable and transferable by other users. Our approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.
45
Schädler, Gerd, and Marcus Breil. "Identification of droughts and heatwaves in Germany with regional climate networks." Nonlinear Processes in Geophysics 28, no. 2 (2021): 231–45. http://dx.doi.org/10.5194/npg-28-231-2021.
Full textAbstract:
Abstract. Regional climate networks (RCNs) are used to identify heatwaves and droughts in Germany and two subregions for the summer half-years and summer seasons of the period 1951 to 2019. RCNs provide information for whole areas (in contrast to the point-wise information from standard indices), the underlying nodes can be distributed arbitrarily, they are easy to construct, and they provide details otherwise difficult to access, like temporal and spatial extent and localisation of extreme events; this makes them suitable for the statistical analysis of climate model output. The RCNs were constructed on the regular 0.25∘ grid of the E-OBS data set. The season-wise correlation of the time series of daily maximum temperature Tmax and precipitation were used to construct the adjacency matrix of the networks. Based on the results of a sensitivity study, we used the edge density, which increases significantly during extreme events, as the main metrics to characterise the network structure. The standard indices for comparison were the Effective Drought Index and Effective Heat Index (EDI and EHI), respectively, based on the same time series and complemented by other published data. Our results show that the RCNs are generally able to identify severe and moderate extremes and can differentiate between regions and seasons.
46
Kadel, Dhaka Ram, and Pawan Kumar Patodiya. "Dynamic Relationship of the Stock Index with the Trading Volume of the Nepal Stock Exchange: An Empirical Analysis." Shanti Journal 3, no. 1-2 (2023): 47–64. http://dx.doi.org/10.3126/shantij.v3i1-2.60799.
Full textAbstract:
Knowledge about the linkage between the volume and the index of the stock is crucial for market participants and investors to make informed decisions in trading and forecasting the stock market, as understanding this fundamental Linkage enhances investment decisions. This study relies on time series data to objectively explore the dynamic linkage between the index of the stock and the amount of trade on the Nepal Stock Exchange from mid December 2018 to mid-January 2023. The outcome of the Autoregressive Distributed Lag (ARDL) model reflects a long- and short-term substantial positive association between the volume of the trade and returns from the stock in the current timeframe. This implies that a change in volume has both a long- and short-term, small but positive effect on the returns from the stock. Conversely, the lagged-period stock index negatively and significantly impacts the present index of the stock. However, the conclusions drawn from the Granger causality test demonstrate no Granger causation from the volume of the trade to return and vice versa, implying that both variables do not affect each other.
47
Patyjewicz, Marta, Devan Mair, Safiya A. Zaloum, et al. "Recreational nitrous oxide and thrombotic events: a case series." BMJ Neurology Open 6, no. 1 (2024): e000619. http://dx.doi.org/10.1136/bmjno-2023-000619.
Full textAbstract:
BackgroundThe study aimed to elucidate the prevalence of nitrous oxide (N2O) usage in patients with unexplained venous thromboembolism (VTE), highlighting the potential association with hyperhomocysteinaemia (HHcy).MethodsWe conducted a retrospective study at the Royal London Hospital, examining cases of N2O-related VTE from March to August 2023. Among 50 patients identified, four (8%) had recent unprovoked VTE. Patient data were collected based on N2O ambulatory emergency care pathway admissions.ResultsAmong the 50 patients identified, four (8%) had recent or concurrent VTE. Three were male (75%), with an ethnic distribution of 50% Asian or Asian British and 50% Black or Black British. Patients were distributed across quintiles of the index of multiple deprivation. All had actual or functional vitamin B12 deficiency.DiscussionThe association between N2O use and VTE requires further investigation, though a plausible mechanism involving HHcy has been proposed. Clinicians should be vigilant for VTE in N2O users, especially those presenting with unexplained symptoms. VTE prophylaxis may be worth considering, particularly if continued exposure to nitrous oxide is anticipated.ConclusionN2O misuse may increase the risk of VTE, warranting attention from healthcare providers. Further research is needed to elucidate this association and inform preventive strategies. Public awareness about the risks of N2O remains essential.
48
Yang, Jing, Peng Hou, Caiqian Yang, Ning Yang, and Kefeng Li. "Damage Identification Method of Box Girder Bridges Based on Distributed Long-Gauge Strain Influence Line under Moving Load." Sensors 21, no. 3 (2021): 915. http://dx.doi.org/10.3390/s21030915.
Full textAbstract:
A new method was proposed for the damage identification of box girder bridges under moving load, wherein the difference of strain influence line (DSIL) was taken as an index to represent the long-gauge strain difference before and after damage. The damage identification theory based on long-gauge strain influence lines was derived for box girder bridges with shear lag effect under consideration, and a regularized index DSIL was proposed for the quantitative identifications of damage location and extent. A series of experiments were carried out to study the influences of speed, vehicle type, and vehicle weight on the damage identification, and the experimental data were obtained by long-gauge fiber Bragg grating strain sensors. Moreover, numerical simulations were performed to confirm the method. The experimental and numerical results show that the method can locate the damage accurately, and quantitatively identify the damage extent under different working conditions. The experimental damage extent is generally slightly higher than the theoretical, with an average identification error smaller than 5%. Additionally, the relative error of damage extent is smaller than 3% under different working conditions. Thus, the effectiveness of this method was verified.
49
Graziano, Maria Daniela, Alfredo Renga, Marco Grasso, and Antonio Moccia. "PRF Selection in Formation-Flying SAR: Experimental Verification on Sentinel-1 Monostatic Repeat-Pass Data." Remote Sensing 12, no. 1 (2019): 29. http://dx.doi.org/10.3390/rs12010029.
Full textAbstract:
Formation-flying synthetic aperture radar (FF-SAR) enables new working modes and can achieve very high performance through a series of very compact, low-weight, satellite platforms thanks to passive operations of conveniently distributed formation-flying receivers. System timing is a crucial aspect of FF-SAR. The manuscript presents a novel approach to pulse repetition frequency (PRF) selection in order to obtain a uniform distribution of samples at given platform positions. A digital beamforming algorithm is applied on a stack of monostatic repeat-pass images collected by the Sentinel-1 system to test the validity of the PRF selection method. Processed images were thus properly selected to achieve the best merit index measuring the quality of samples distribution. The results show that: (a) the image resulting from beamforming features better azimuth ambiguity-to-signal ratio and (b) the proposed approach for PRF selection allows one to individuate a subset of the available images leading to uniform distribution of samples which can be used to support FF-SAR processing.
50
Khan, M. T. "Economic Factors Influencing Housing Prices in Pakistan." Review of Business and Economics Studies 11, no. 3 (2023): 49–67. http://dx.doi.org/10.26794/2308-944x-2023-11-3-49-67.
Full textAbstract:
The aim of this research is to explore the factors influencing housing prices in Pakistan. The author used monthly time series data for the period from 2011 to 2020, which were obtained from different sources: housing prices data from zameen.com, Karachi interbank offered rate (KIBOR) as a proxy for monetary policy, consumer price index as a proxy for inflation, and exchange rate data from the State Bank of Pakistan. Various methods, such as autoregressive distributed lag (ARDL), comparative analysis and deductive analysis were employed. Before using the ARDL technique, a proper lag length was selected, which turned out to be 11 months. Various diagnostic tests indicated model stability with no autocorrelation or structural breaks. The author concluded that the KIBOR rate negatively affected housing prices, while inflation and exchange rates affected house prices positively.