scholarly journals Utilizing homogenized observation records and reconstructed time series data to estimate recent trends in Mid-Atlantic soil moisture scarcity

2020 ◽  
Author(s):  
Robert Kennedy Smith ◽  
José A. Guijarro ◽  
Der-Chen Chang
Keyword(s):  
Soil Moisture ◽  
Extreme Events ◽  
Time Series Data ◽  
Temporal Correlation ◽  
Daily Basis ◽  
Series Data ◽  
Irrigation Water Demand ◽  
Drought Conditions ◽  
The Usa ◽  
Reconstructed Time Series

AbstractThe Mid-Atlantic region of the USA has experienced increasing annual precipitation amounts in recent decades, along with more frequent extreme events of greater magnitude. Unlike many US regions that have suffered increasing drought conditions from higher evapotranspiration demand, positive trends in the Mid-Atlantic accumulated precipitation are greater than the recent increases in reference evapotranspiration. The temporal correlation between precipitation events and soil moisture capacity is essential for determining how the nature of drought has changed in the region. This analysis has shown that soil moisture scarcity has declined in nine of ten subregions of the Mid-Atlantic that were analyzed from 1985 to 2019. Two algorithms were deployed to draw this conclusion: Climatol enabled the use of the FAO-56 Penman-Monteith equation on daily observation station data for which complete records were unavailable, and the second algorithm calculated soil moisture levels on a daily basis, more accurately capturing drought conditions than common methods using weekly or monthly summaries. Although the declining drought trends were not statistically significant, a result of more extreme events and higher evapotranspiration rates, the inclusion of direct data from an expanded set of locations provides greater clarity from the trends, allowing policymakers and landowners to anticipate changes in future Mid-Atlantic irrigation water demand.

A new approach to detect extreme events: a case study using remotely-sensed precipitation time-series data

2021 ◽  
Vol 24 ◽  
pp. 100618
Author(s):  
Philipe Riskalla Leal ◽  
Ricardo José de Paula Souza e Guimarães ◽  
Fábio Dall Cortivo ◽  
Rayana Santos Araújo Palharini ◽  
Milton Kampel
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Time Series Data ◽  
Remotely Sensed ◽  
Series Data ◽  
Precipitation Time Series ◽  
New Approach

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.

A deep learning method for data recovery in sensor networks using effective spatio-temporal correlation data

Sensor Review ◽  
2019 ◽  
Vol 39 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Jinghan Du ◽  
Haiyan Chen ◽  
Weining Zhang
Keyword(s):  
Deep Learning ◽  
Sensor Networks ◽  
Large Scale ◽  
Time Series Data ◽  
Temporal Correlation ◽  
Data Recovery ◽  
Series Data ◽  
Learning Method ◽  
Content Type ◽  
Spatio Temporal

Purpose In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks. Design/methodology/approach Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor networks based on a deep learning method, i.e. deep belief network (DBN). Specifically, when one sensor fails, the historical time-series data of its own and the real-time data from surrounding sensor nodes, which have high similarity with a failure observed using the proposed similarity filter, are collected first. Then, the high-level feature representation of these spatio-temporal correlation data is extracted by DBN. Moreover, to determine the structure of a DBN model, a reconstruction error-based algorithm is proposed. Finally, the missing data are predicted based on these features by a single-layer neural network. Findings This paper collects a noise data set from an airport monitoring system for experiments. Various comparative experiments show that the proposed algorithms are effective. The proposed data recovery model is compared with several other classical models, and the experimental results prove that the deep learning-based model can not only get a better prediction accuracy but also get a better performance in training time and model robustness. Originality/value A deep learning method is investigated in data recovery task, and it proved to be effective compared with other previous methods. This might provide a practical experience in the application of a deep learning method.

scholarly journals Predicting Financial Extremes Based on Weighted Visual Graph of Major Stock Indices

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Dong-Rui Chen ◽  
Chuang Liu ◽  
Yi-Cheng Zhang ◽  
Zi-Ke Zhang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Financial Market ◽  
Extreme Events ◽  
Time Series Data ◽  
Graph Algorithm ◽  
Series Data ◽  
Financial Bubbles ◽  
Stock Indices ◽  
Visual Graph

Understanding and predicting extreme turning points in the financial market, such as financial bubbles and crashes, has attracted much attention in recent years. Experimental observations of the superexponential increase of prices before crashes indicate the predictability of financial extremes. In this study, we aim to forecast extreme events in the stock market using 19-year time-series data (January 2000–December 2018) of the financial market, covering 12 kinds of worldwide stock indices. In addition, we propose an extremes indicator through the network, which is constructed from the price time series using a weighted visual graph algorithm. Experimental results on 12 stock indices show that the proposed indicators can predict financial extremes very well.

Returns and volume

2020 ◽  
Vol 37 (3) ◽  
pp. 457-473
Author(s):  
Panos Fousekis
Keyword(s):  
Trading Volume ◽  
Time Series Data ◽  
Price Volatility ◽  
Statistical Testing ◽  
Model Specification ◽  
Series Data ◽  
Causal Order ◽  
Content Type ◽  
Daily Data ◽  
The Usa

Purpose The relationship between returns and trading volume is central in financial economics because it has both a theoretical interest and important practical implications with regard to the structure of financial markets and the level of speculation activity. The aim of this study is to provide new insights into the association between returns and trading volume by investigating their kernel (instantaneous) causality. The empirical analysis relies on time series data from 22 commodities futures markets (agricultural, energy and metals) in the USA. Design/methodology/approach Non-parametric (local linear) regressions are applied to daily data on returns and on trading activity; generalized correlation measures are computed and their differences are subjected to formal statistical testing. Findings The results suggest that raw returns are likely to kernel-cause volume and volume is likely to kernel-cause price volatility. The patterns of causal order are generally in line with what is stipulated by the relevant theory, they provide guidance for model specification and they appear to explain the empirical evidence on temporal (lag-lead) causality between the same pairs of variables obtained in earlier works. Originality/value The concept of kernel causality has very recently become a part of the toolkit for econometric/statistical analysis. To the best of the author’s knowledge, this is the first study that relies on the notion of kernel (instantaneous) causality to provide new evidence on a relationship that is of keen interest to investors, professional economists and policymakers.

scholarly journals Forecasting the Spread of COVID-19 Pandemic with Prophet

2021 ◽  
Vol 35 (2) ◽  
pp. 115-122
Author(s):  
Mohan Mahanty ◽  
K. Swathi ◽  
K. Sasi Teja ◽  
P. Hemanth Kumar ◽  
A. Sravani
Keyword(s):  
Data Science ◽  
Statistical Data ◽  
Time Series Data ◽  
Daily Basis ◽  
Series Data ◽  
Use Case ◽  
The Past ◽  
Open Source Framework ◽  
Forecast Models ◽  
Using Data

COVID-19 pandemic shook the whole world with its brutality, and the spread has been still rising on a daily basis, causing many nations to suffer seriously. This paper presents a medical stance on research studies of COVID-19, wherein we estimated a time-series data-based statistical model using prophet to comprehend the trend of the current pandemic in the coming future after July 29, 2020 by using data at a global level. Prophet is an open-source framework discovered by the Data Science team at Facebook for carrying out forecasting based operations. It aids to automate the procedure of developing accurate forecasts and can be customized according to the use case we are solving. The Prophet model is easy to work because the official repository of prophet is live on GitHub and is open for contributions and can be fitted effortlessly. The statistical data presented on the paper refers to the number of daily confirmed cases officially for the period January 22, 2020, to July 29, 2020. The estimated data produced by the forecast models can then be used by Governments and medical care departments of various countries to manage the existing situation, thus trying to flatten the curve in various nations as we believe that there is minimal time to do this. The inferences made using the model can be clearly comprehended without much effort. Furthermore, it tries to give an understanding of the past, present, and future trends by showing graphical forecasts and statistics. Compared to other models, prophet specifically holds its own importance and innovativeness as the model is fully automated and generates quick and precise forecasts that can be tunable additionally.

scholarly journals GA-Optimized Multivariate CNN-LSTM Model for Predicting Multi-channel Mobility in the COVID-19 Pandemic

2021 ◽  
Vol 5 (5) ◽  
pp. 619-635
Author(s):  
Harya Widiputra
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Human Mobility ◽  
Positive Association ◽  
Human Movement ◽  
Daily Basis ◽  
Series Data ◽  
Data Prediction ◽  
The Future

The primary factor that contributes to the transmission of COVID-19 infection is human mobility. Positive instances added on a daily basis have a substantial positive association with the pace of human mobility, and the reverse is true. Thus, having the ability to predict human mobility trend during a pandemic is critical for policymakers to help in decreasing the rate of transmission in the future. In this regard, one approach that is commonly used for time-series data prediction is to build an ensemble with the aim of getting the best performance. However, building an ensemble often causes the performance of the model to decrease, due to the increasing number of parameters that are not being optimized properly. Consequently, the purpose of this study is to develop and evaluate a deep learning ensemble model, which is optimized using a genetic algorithm (GA) that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM). A CNN is used to conduct feature extraction from mobility time-series data, while an LSTM is used to do mobility prediction. The parameters of both layers are adjusted using GA. As a result of the experiments conducted with data from the Google Community Mobility Reports in Indonesia that ranges from the beginning of February 2020 to the end of December 2020, the GA-Optimized Multivariate CNN-LSTM ensemble outperforms stand-alone CNN and LSTM models, as well as the non-optimized CNN-LSTM model, in terms of predicting human movement in the future. This may be useful in assisting policymakers in anticipating future human mobility trends. Doi: 10.28991/esj-2021-01300 Full Text: PDF

scholarly journals MEASURING POVERTY CYCLES IN THE U.S. 1959–2013

2018 ◽  
Vol 24 (4) ◽  
pp. 1737-1754 ◽  
Author(s):  
Marinko Škare ◽  
Romina Pržiklas Družeta ◽  
Damian Škare
Keyword(s):  
Business Cycles ◽  
Time Series Data ◽  
Well Being ◽  
Poverty Rate ◽  
Series Data ◽  
Poverty Dynamics ◽  
The Usa ◽  
Shed Light ◽  
The U.S ◽  
Economic Well Being

This paper aims to shed light on the nature of poverty as a dynamic process by examining poverty cycles, their magnitudes, and their asymmetry. The designated benchmark country is the USA due to the availability of time series data making comprehensive analyses possible. We use Harding and Pagan (2002) and the Cardinale and Taylor (2009) model to isolate poverty cycles in the U.S. during 1959–2013. Once isolated, we test the poverty cycles for duration dependency, and their synchronization with the U.S. business cycles observed over the same period. We find that poverty dynamics measured through poverty cycles differ for alternative poverty rate indicators. Another critical point is the magnitude of change in the poverty cycles. Prolonged and more volatile poverty cycles have a significant adverse impact on people and families facing them. That is particularly important for policymakers who should rethink poverty policy guidelines aimed at helping people with more volatile poverty cycles first. Our is the first study, to our knowledge, to isolate poverty cycles and focus on their nature. Poverty cycles should attract more attention from policymakers since they more accurately assess nations’ economic well-being than output (GDP).

scholarly journals The durability of economic indicators in container shipping demand: a case study of East Asia–US container transport

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tomoya Kawasaki ◽  
Takuma Matsuda ◽  
Yui-yip Lau ◽  
Xiaowen Fu
Keyword(s):  
Time Series ◽  
East Asia ◽  
Time Series Data ◽  
Economic Indicators ◽  
Series Data ◽  
Container Shipping ◽  
Content Type ◽  
The Usa ◽  
Container Movement ◽  
The Impact

Purpose In the maritime industry, it is vital to have a reliable forecast of container shipping demand. Although indicators of economic conditions have been used in modeling container shipping demand on major routes such as those from East Asia to the USA, the duration of such indicators’ effects on container movement demand have not been systematically examined. To bridge this gap in research, this study aims to identify the important US economic indicators that significantly affect the volume of container movements and empirically reveal the duration of such impacts. Design/methodology/approach The durability of economic indicators on container movements is identified by a vector autoregression (VAR) model using monthly-based time-series data. In the VAR model, this paper can analyze the effect of economic indicators at t-k on container movement at time t. In the model, this paper considers nine US economic indicators as explanatory variables that are likely to affect container movements. Time-series data are used for 228 months from January 2001 to December 2019. Findings In the mainland China route, “building permission” receives high impact and has a duration of 14 months, reflecting the fact that China exports a high volume of housing-related goods to the USA. Regarding the South Korea and Japan routes, where high volumes of machinery goods are exported to the USA, the “index of industrial production” receives a high impact with 11 and 13 months’ duration, respectively. On the Taiwan route, as several types of goods are transported with significant shares, “building permits” and “index of industrial production” have important effects. Originality/value Freight demand forecasting for bulk cargo is a popular research field because of the public availability of several time-series data. However, no study to date has measured the impact and durability of economic indicators on container movement. To bridge the gap in the literature in terms of the impact of economic indicators and their durability, this paper developed a time-series model of the container movement from East Asia to the USA.

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