scholarly journals Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 173
Author(s):  
Cong Li ◽  
Yaonan Zhang ◽  
Xupeng Ren
Keyword(s):  
Soil Temperature ◽  
Land Surface ◽  
Meteorological Parameters ◽  
Time Series Data ◽  
The United States ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Prediction Ability ◽  
Daily Average ◽  
Biogeochemical Models

Soil temperature (ST) plays a key role in the processes and functions of almost all ecosystems, and is also an essential parameter for various applications such as agricultural production, geothermal development, and their utilization. Although numerous machine learning models have been used in the prediction of ST, and good results have been obtained, most of the current studies have focused on daily or monthly ST predictions, while hourly ST predictions are scarce. This paper presents a novel scheme for forecasting the hourly ST using weather forecast data. The method considers the hourly ST prediction to be the superposition of two parts, namely, the daily average ST prediction and the ST amplitude (the difference between the hourly ST and the daily average ST) prediction. According to the results of correlation analysis, we selected nine meteorological parameters and combined two temporal parameters as the input vectors for predicting the daily average ST. For the task of predicting the ST amplitude, seven meteorological parameters and one temporal parameter were selected as the inputs. Two submodels were constructed using a deep bidirectional long short-term memory network (BiLSTM). For the task of hourly ST prediction at five different soil depths at 30 sites, which are located in 5 common climates in the United States, the results showed the method proposed in this paper performs best at all depths for 30 stations (100% of all) for the root mean square error (RMSE), 27 stations (90% of all) for the mean absolute error (MAE), and 30 stations (100% of all) for the coefficient of determination (R2), respectively. Moreover, the method adopted in this study displays a stronger ST prediction ability than the traditional methods under all climate types involved in the experiment, the hourly ST produced by it can be used as a driving parameter for high-resolution biogeochemical models, land surface models and hydrological models and can provide ideas for an analysis of other time series data.

scholarly journals Canada One Water: integrated groundwater-surface-water-climate modelling for climate change adaptation

2021 ◽  
Author(s):  
H A J Russell ◽  
S K Frey
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Time Series Data ◽  
Regional Scale ◽  
Model Development ◽  
Climate Scenario ◽  
The United States ◽  
Series Data ◽  
Climate Modelling ◽  
Community Land Model

Canada 1 Water is a 3-year governmental multi-department-private-sector-academic collaboration to model the groundwater-surface-water of Canada coupled with historic climate and climate scenario input. To address this challenge continental Canada has been allocated to one of 6 large watershed basins of approximately two million km2. The model domains are based on natural watershed boundaries and include approximately 1 million km2 of the United States. In year one (2020-2021) data assembly and validation of some 20 datasets (layers) is the focus of work along with conceptual model development. To support analysis of the entire water balance the modelling framework consists of three distinct components and modelling software. Land Surface modelling with the Community Land Model will support information needed for both the regional climate modelling using the Weather Research & Forecasting model (WRF), and input to HydroGeoSphere for groundwater-surface-water modelling. The inclusion of the transboundary watersheds will provide a first time assessment of water resources in this critical international domain. Modelling is also being integrated with Remote Sensing datasets, notably the Gravity Recovery and Climate Experiment (GRACE). GRACE supports regional scale watershed analysis of total water flux. GRACE along with terrestrial time-series data will serve provide validation datasets for model results to ensure that the final project outputs are representative and reliable. The project has an active engagement and collaborative effort underway to try and maximize the long-term benefit of the framework. Much of the supporting model datasets will be published under open access licence to support broad usage and integration.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

scholarly journals Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

2021 ◽  
Vol 18 (9) ◽  
pp. 4560
Author(s):  
Mostafa Abbas ◽  
Thomas B. Morland ◽  
Eric S. Hall ◽  
Yasser EL-Manzalawy
Keyword(s):  
Functional Data ◽  
Time Series Data ◽  
Principal Component ◽  
State Level ◽  
The United States ◽  
Functional Principal Component Analysis ◽  
Series Data ◽  
Dynamic Correlation ◽  
The Us ◽  
Google Search

We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

scholarly journals ANALISIS FAKTOR PENENTU INDEKS HARGA SAHAM GABUNGAN DI INDONESIA

2019 ◽  
Vol 1 (4) ◽  
pp. 37
Author(s):  
Yulizar Fikri ◽  
Ali Anis
Keyword(s):  
Economic Growth ◽  
Foreign Exchange ◽  
Stock Price ◽  
Price Index ◽  
Time Series Data ◽  
Secondary Data ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Foreign Exchange Reserves ◽  
Stock Price Index

This study aims to determine the analysis of the determinants of the composite stock price index in Indonesia. The independent variables in this study are inflation as X1, foreign exchange reserves as X2, exchange rates as X3, and economic growth as X4, and the dependent variable of the composite stock price index as Y. The data used are secondary data in the formof time series data from 2010Q1 until 2019Q2, with data collection techniques, namely documentation from Bank Indonesia publications, the Central Statistics Agency, investing. comsite and library research. The research methods used are: (1) Multiple Linear Regression, (2) Classical Assumption Test (3) coefficient of determination. The results of this study indicate that:(1) inflation does not significantly influence the composite stock price index. (2) foreign exchange reserves have a significant positive effect on the composite stock price index. (3) the rupiah exchange rate has an influence on the composite stock price index and (4) economic growth hasno significant effect on the composite stock price index.

scholarly journals The 2010 Structural-Demographic Forecast for the 2010–2020 Decade: A Retrospective Assessment

2020 ◽  
Author(s):  
Peter Turchin ◽  
Andrey Korotayev
Keyword(s):  
Western Europe ◽  
Time Series Data ◽  
Political Instability ◽  
The United States ◽  
Stress Index ◽  
Series Data ◽  
Strongly Correlated ◽  
Retrospective Assessment ◽  
The United Kingdom ◽  
The Usa

This article revisits the prediction, made in 2010, that the 2010–2020 decade would likely be a period of growing instability in the United States and Western Europe (Turchin 2010). This prediction was based on a computational model that quantified in the USA such structural-demographic forces for instability as popular immiseration, intraelite competition, and state weakness prior to 2010. Using these trends as inputs, the model calculated and projected forward in time the Political Stress Index, which in the past was strongly correlated with socio-political instability. Ortmans et al. (2017) conducted a similar structural-demographic study for the United Kingdom and obtained similar results. Here we use the Cross-National Time-Series Data Archive for the US, UK, and Western European countries to assess these structural-demographic predictions. We find that such measures of socio-political instability as anti-government demonstrations and riots increased dramatically during the 2010–2020 decade in all of these countries.

scholarly journals MENINGKATKAN KINERJA KEUANGAN RETURN ON EQUITY (ROE) MELALUI RASIO STRUKTUR MODAL DEBT TO ASSET RATIO (DAR) DAN DEBT TO EQUITY RATIO (DER) PADA PT BANK MANDIRI TBK YANG TERDAFTAR DI BURSA EFEK INDONESIA

2021 ◽  
Vol 12 (1) ◽  
pp. 52-65
Author(s):  
Armalinda Armalinda
Keyword(s):  
Financial Performance ◽  
Quantitative Research ◽  
Time Series Data ◽  
Stock Exchange ◽  
Statistical Tests ◽  
Financial Statements ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Return On Equity ◽  
Equity Ratio

This study aims to determine how much influence the Debt to Assets Ratio (DAR) and Debt to Equity Ratio (DER) have on the Return on Equity (ROE) of PT Bank Mandiri Tbk which are listed on the Indonesia Stock Exchange. The research design used in this research is associative/quantitative research. The population in this study is the annual financial statements of PT. Bank Mandiri Tbk for the period 2012-2019, while the sample was taken using time series data, namely the annual financial statements of PT. Bank Mandiri Tbk for the period 2012-2019 which consists of balance statements, income statements, and cash flow from funding activities from 2012 to 2019. The result of the coefficient of determination (R Square) is 0.813. This figure means that 0.813 or 81.3% of the diversity of data from financial performance data can be explained by the two independent variables, namely the Debt to Asset Ratio and the Debt to Equity Ratio. While the rest (1-0.813 = 0.817) or 18.7% is explained by other factors outside the study. The results of statistical tests show that the Asset Ratio and Debt to Equity Ratio together (simultaneously) have an effect on financial performance (Return on Equity).

scholarly journals PENGARUH EKUIVALEN RATE BAGI HASIL, TINGKAT SUKU BUNGA DEPOSITO BPR DAN JUMLAH KANTOR BPRS TERHADAP JUMLAH NASABAH DEPOSITO iB MUDHARABAH PADA BPRS DI INDONESIA PERIODE 2016-2018

2019 ◽  
Vol 3 (2) ◽  
pp. 124-139
Author(s):  
Juliana Putri ◽  
Salman Alfarisi
Keyword(s):  
Interest Rates ◽  
Time Series Data ◽  
Predictive Analytics ◽  
Linear Regression Analysis ◽  
Profit Sharing ◽  
Variable Number ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Equivalent Rate ◽  
Rate Of Profit

This study aims to determine the effect of the equivalent rate of profit sharing, interest rates on BPR deposits and the number of BPRS Offices on the number of mudharabah iB deposit customers at BPRS in Indonesia. The research method used is quantitative descriptive research using secondary data in the form of financial reports published by OJK in Sharia Banking Statistics (SPS) and Indonesian Banking Statistics (SPI) with time series data in the period of 2016-2018. The sample in this study all BPRS in Indonesia is 168 BPRS. Analysis of research using multiple linear regression analysis using application or supporting software namely PASW (Predictive Analytics SoftWare) Statistics 18, the results of research, it can be concluded that: 1) Equivalent rate of profit sharing (X1) has a significant negative effect of iB mudharabah deposit customers, 2) Variable interest rates on BPR deposits (X2) do not affect the number of mudharabah iB deposit customers. 3) The variable number of BPRS offices (X3) has a significant positive effect on the number of mudharabah iB deposit customers. 4) The coefficient of determination obtained is 0.586 or 58.6%. which means that 58.6% causes variable variable number of iB mudharabah (Y) deposit customers can be influenced by the equivalent rate of profit sharing, the level of BPR deposit rates and the number of BPRS offices, while the remaining 41.4% is influenced by other factors not included in the study.

Gendered Funding: United Way Board Composition and the Funding of Women- and Girl-Serving Organizations

2021 ◽  
pp. 089976402110574
Author(s):  
Lauren Dula
Keyword(s):  
Time Series Data ◽  
Critical Mass ◽  
Interrupted Time Series ◽  
The United States ◽  
Women In Leadership ◽  
Representative Bureaucracy ◽  
Series Data ◽  
Leadership Positions ◽  
United Way ◽  
Bureaucracy Theory

Representative bureaucracy theory posits that the passive representation of women in leadership positions will lead to active representation of the concerns of women in general. This article attempts to identify whether this theory plays out on boards of nonprofit funding organizations, specifically United Ways across the United States. Using random effects modeling of interrupted time series data covering 15 years, the findings suggest a small yet significant nonlinear effect of women in leadership positions on boards upon the size of funding for women- and girl-serving organizations. This partially supports representative bureaucracy theory, but raises questions as to why there is a negative representational effect past a certain “critical mass” of women.

Abstract 19225: Impact of Change in Resuscitation Guidelines on National Out-of-hospital Cardiac Arrest Outcomes: Fulfilled Expectations?

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Shaker M Eid ◽  
Aiham Albaeni ◽  
Rebeca Rios ◽  
May Baydoun ◽  
Bolanle Akinyele ◽  
...  
Keyword(s):  
Time Series ◽  
Cardiac Arrest ◽  
Time Series Data ◽  
Statistical Significance ◽  
Interrupted Time Series ◽  
The United States ◽  
Series Data ◽  
National Database ◽  
Resuscitation Guidelines ◽  
Hospital Cardiac Arrest

Background: The intent of the 5-yearly Resuscitation Guidelines is to improve outcomes. Previous studies have yielded conflicting reports of a beneficial impact of the 2005 guidelines on out-of-hospital cardiac arrest (OHCA) survival. Using a national database, we examined survival before and after the introduction of both the 2005 and 2010 guidelines. Methods: We used the 2000 through 2012 National Inpatient Sample database to select patients ≥18 years admitted to hospitals in the United States with non-traumatic OHCA (ICD-9 CM codes 427.5 & 427.41). A quasi-experimental (interrupted time series) design was used to compare monthly survival trends. Outcomes for OHCA were compared pre- and post- 2005 and 2010 resuscitation guidelines release as follows: 01/2000-09/2005 vs. 10/2005-9/2010 and 10/2005-9/2010 vs. 10/2010-12/2012. Segmented regression analyses of interrupted time series data were performed to examine changes in survival to hospital discharge. Results: For the pre- and post- guidelines periods, 81600, 69139 and 36556 patients respectively survived to hospital admission following OHCA. Subsequent to the release of the 2005 guidelines, there was a statistically significant worsening in survival trends (β= -0.089, 95% CI -0.163 – -0.016, p =0.018) until the release of the 2010 guidelines when a sharp increase in survival was noted which persisted for the period of study (β= 0.054, 95% CI -0.143 – 0.251, p =0.588) but did not achieve statistical significance (Figure). Conclusion: National clinical guidelines developed to impact outcomes must include mechanisms to assess whether benefit actually occurs. The worsening in OHCA survival following the 2005 guidelines is thought provoking but the improvement following the release of the 2010 guidelines is reassuring and worthy of perpetuation.

scholarly journals Exchange Rate Dynamics and the Bilateral Trade Balance: The Case of U.S. Agriculture

2009 ◽  
Vol 38 (2) ◽  
pp. 213-228 ◽  
Author(s):  
Jungho Baek ◽  
Won W. Koo ◽  
Kranti Mulik
Keyword(s):  
United States ◽  
Exchange Rate ◽  
Bilateral Trade ◽  
Time Series Data ◽  
The United States ◽  
Agricultural Trade ◽  
Agricultural Products ◽  
Series Data ◽  
J Curve ◽  
Trading Partners

This study examines the dynamic effects of changes in exchange rates on bilateral trade of agricultural products between the United States and its 15 major trading partners. Special attention is paid to investigate whether or not the J-curve hypothesis holds for U.S. agricultural trade. For this purpose, an autoregressive distributed lag (ARDL) approach to cointegration is applied to quarterly time-series data from 1989 and 2007. Results show that the exchange rate plays a crucial role in determining the short- and long-run behavior of U.S. agricultural trade. However, we find little evidence of the J-curve phenomenon for U.S. agricultural products with the United States’ major trading partners.

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