scholarly journals Transport Catastrophe Analysis as an Alternative to a Monofractal Description: Theory and Application to Financial Crisis Time Series

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Sergey A. Kamenshchikov
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Large Scale ◽  
Extreme Values ◽  
Phase Diffusion ◽  
Description Theory ◽  
Mortgage Crisis ◽  
Memory Measure ◽  
Short Memory ◽  
Scale Failure

The goal of this investigation was to overcome limitations of a persistency analysis, introduced by Benoit Mandelbrot for monofractal Brownian processes: nondifferentiability, Brownian nature of process, and a linear memory measure. We have extended a sense of a Hurst factor by consideration of a phase diffusion power law. It was shown that precatastrophic stabilization as an indicator of bifurcation leads to a new minimum of momentary phase diffusion, while bifurcation causes an increase of the momentary transport. An efficiency of a diffusive analysis has been experimentally compared to the Reynolds stability model application. An extended Reynolds parameter has been introduced as an indicator of phase transition. A combination of diffusive and Reynolds analyses has been applied for a description of a time series of Dow Jones Industrial weekly prices for the world financial crisis of 2007–2009. Diffusive and Reynolds parameters showed extreme values in October 2008 when a mortgage crisis was fixed. A combined R/D description allowed distinguishing of market evolution short-memory and long-memory shifts. It was stated that a systematic large scale failure of a financial system has begun in October 2008 and started fading in February 2009.

scholarly journals Copula-based statistical refinement of precipitation in RCM simulations over complex terrain

2011 ◽  
Vol 15 (7) ◽  
pp. 2401-2419 ◽  
Author(s):  
P. Laux ◽  
S. Vogl ◽  
W. Qiu ◽  
H. R. Knoche ◽  
H. Kunstmann
Keyword(s):  
Time Series ◽  
Complex Terrain ◽  
Large Scale ◽  
Extreme Values ◽  
Regional Climate ◽  
Tail Dependence ◽  
Copula Models ◽  
Climate Simulations ◽  
Weather Situation ◽  
The Common

Abstract. This paper presents a new Copula-based method for further downscaling regional climate simulations. It is developed, applied and evaluated for selected stations in the alpine region of Germany. Apart from the common way to use Copulas to model the extreme values, a strategy is proposed which allows to model continuous time series. As the concept of Copulas requires independent and identically distributed (iid) random variables, meteorological fields are transformed using an ARMA-GARCH time series model. In this paper, we focus on the positive pairs of observed and modelled (RCM) precipitation. According to the empirical copulas, significant upper and lower tail dependence between observed and modelled precipitation can be observed. These dependence structures are further conditioned on the prevailing large-scale weather situation. Based on the derived theoretical Copula models, stochastic rainfall simulations are performed, finally allowing for bias corrected and locally refined RCM simulations.

scholarly journals Copula-based statistical refinement of precipitation in RCM simulations over complex terrain

2011 ◽  
Vol 8 (2) ◽  
pp. 3001-3045 ◽  
Author(s):  
P. Laux ◽  
S. Vogl ◽  
W. Qiu ◽  
H. R. Knoche ◽  
H. Kunstmann
Keyword(s):  
Time Series ◽  
Complex Terrain ◽  
Large Scale ◽  
Extreme Values ◽  
Regional Climate ◽  
Copula Models ◽  
Climate Simulations ◽  
Weather Situation ◽  
The Common ◽  
The Impact

Abstract. This paper presents a new Copula-based method for further downscaling regional climate simulations. It is developed, applied and evaluated for selected stations in the alpine region of Germany. Apart from the common way to use Copulas to model the extreme values, a strategy is proposed which allows to model continous time series. In this paper, we focus on the positive pairs of observed and modelled (RCM) precipitation. As the concept of Copulas requires independent and identically distributed (iid) random variables, meteorological fields are transformed using an ARMA-GARCH time series model. The dependence structures between modelled and observed precipitation are conditioned on the prevailing large-scale weather situation. The impact of the altitude of the stations and their distance to the surrounding modelled grid cells is analyzed. Based on the derived theoretical Copula models, stochastic rainfall simulations are performed, finally allowing for bias corrected and locally refined RCM simulations.

scholarly journals Identifying global patterns of stochasticity and nonlinearity in the Earth System

2016 ◽  
Author(s):  
Fernando Arizmendi ◽  
Marcelo Barreiro ◽  
Cristina Masoller
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Distance Measure ◽  
Extreme Values ◽  
Surface Air Temperature ◽  
Entropy Measure ◽  
The Earth ◽  
Geographical Regions ◽  
Atmospheric Phenomena ◽  
Low Entropy

Abstract. We demonstrate that two simple measures of time series analysis are able to capture different dynamical and statistical properties of large-scale atmospheric phenomena. We consider two surface air temperature (SAT) datasets, covering a spatial grid of points over the Earth surface (NCEP CDAS1 and ERA Interim reanalysis). In each location we analyze i) the distance between the lagged SAT time series and the insolation (i.e., the local top-of-atmosphere incoming solar radiation), and ii) the Shannon entropy computed from the probability distribution function (pdf) of SAT values. The distance quantifies the similarity between the lagged SAT waveform and the isolation waveform, while the entropy, as defined in information theory, measures the degree of disorder or uncertainty of each time series, which we refer to as stochasticity: the entropy captures the shape of the SAT pdf and is maximum when the pdf is uniform. With the distance measure we uncover well-defined spatial patterns formed by regions with similar SAT response to solar forcing, while with the entropy measure, we uncover regions that have SAT pdf of similar shape. The entropy analysis also allows identifying the geographical regions in which SAT time series has extreme values (i.e., values which are extreme in the local statistics), because the long-tail shape of the pdf is captured as low entropy values. We uncover significant differences between the NCEP CDAS1 and ERA Interim datasets in specific geographical regions, which are due to the presence of extreme values in one dataset but not in the other. In this way, the entropy maps are a valuable tool for anomaly detection and model inter-comparisons.

scholarly journals NEO: NEuro-Inspired Optimization—A Fractional Time Series Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Sarthak Chatterjee ◽  
Subhro Das ◽  
Sérgio Pequito
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Optimization Problems ◽  
Practical Applications ◽  
Power Law Decay ◽  
Time Series Approach ◽  
Short Memory ◽  
Exponential Power ◽  
Memory Characteristics ◽  
Recurrent Theme

Solving optimization problems is a recurrent theme across different fields, including large-scale machine learning systems and deep learning. Often in practical applications, we encounter objective functions where the Hessian is ill-conditioned, which precludes us from using optimization algorithms utilizing second-order information. In this paper, we propose to use fractional time series analysis methods that have successfully been used to model neurophysiological processes in order to circumvent this issue. In particular, the long memory property of fractional time series exhibiting non-exponential power-law decay of trajectories seems to model behavior associated with the local curvature of the objective function at a given point. Specifically, we propose a NEuro-inspired Optimization (NEO) method that leverages this behavior, which contrasts with the short memory characteristics of currently used methods (e.g., gradient descent and heavy-ball). We provide evidence of the efficacy of the proposed method on a wide variety of settings implicitly found in practice.

Weather Generators

2020 ◽  
Author(s):  
Shuiqing Yin ◽  
Deliang Chen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Large Scale ◽  
Extreme Values ◽  
Future Climate Change ◽  
Continuous Simulation ◽  
Weather Generators ◽  
Change Impact ◽  
Multiple Variables ◽  
Climate Time Series

Weather generators (WGs) are stochastic models that can generate synthetic climate time series of unlimited length and having statistical properties similar to those of observed time series for a location or an area. WGs can infill missing data, extend the length of climate time series, and generate meteorological conditions for unobserved locations. Since the 1990s WGs have become an important spatial-temporal statistical downscaling methodology and have been playing an increasingly important role in climate-change impact assessment. Although the majority of the existing WGs have focused on simulation of precipitation for a single site, more and more WGs considering correlations among multiple sites, and multiple variables, including precipitation and nonprecipitation variables such as temperature, solar radiation, wind, humidity, and cloud cover have been developed for daily and sub-daily scales. Various parametric, semi-parametric and nonparametric WGs have shown the ability to represent the mean, variance, and autocorrelation characteristics of climate variables at different scales. Two main methodologies including change factor and conditional WGs on large-scale dynamical and thermal dynamical weather states have been developed for applications under a changing climate. However, rationality and validity of assumptions underlining both methodologies need to be carefully checked before they can be used to project future climate change at local scale. Further, simulation of extreme values by the existing WGs needs to be further improved. WGs assimilating multisource observations from ground observations, reanalysis, satellite remote sensing, and weather radar for the continuous simulation of two-dimensional climate fields based on the mixed physics-based and stochastic approaches deserve further efforts. An inter-comparison project on a large ensemble of WG methods may be helpful for the improvement of WGs. Due to the applied nature of WGs, their future development also requires inputs from decision-makers and other relevant stakeholders.

EXTREME VALUES OF SEA SURFACE TEMPERATURE ASSOCIATED WITH LONG-PERIOD PHENOMENA OCCURRED DURING 1960-2015 IN THE COLOMBIAN PACIFIC OCEAN

2017 ◽  
Author(s):  
Diaz Juan Navia ◽  
Diaz Juan Navia ◽  
Bolaños Nancy Villegas ◽  
Bolaños Nancy Villegas ◽  
Igor Malikov ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Extreme Values ◽  
Sea Surface ◽  
Absolute Maximum ◽  
Data Set ◽  
Arctic Oscillation Index ◽  
Colombian Pacific

Sea Surface Temperature Anomalies (SSTA), in four coastal hydrographic stations of Colombian Pacific Ocean, were analyzed. The selected hydrographic stations were: Tumaco (1°48'N-78°45'W), Gorgona island (2°58'N-78°11'W), Solano Bay (6°13'N-77°24'W) and Malpelo island (4°0'N-81°36'W). SSTA time series for 1960-2015 were calculated from monthly Sea Surface Temperature obtained from International Comprehensive Ocean Atmosphere Data Set (ICOADS). SSTA time series, Oceanic Nino Index (ONI), Pacific Decadal Oscillation index (PDO), Arctic Oscillation index (AO) and sunspots number (associated to solar activity), were compared. It was found that the SSTA absolute minimum has occurred in Tumaco (-3.93°C) in March 2009, in Gorgona (-3.71°C) in October 2007, in Solano Bay (-4.23°C) in April 2014 and Malpelo (-4.21°C) in December 2005. The SSTA absolute maximum was observed in Tumaco (3.45°C) in January 2002, in Gorgona (5.01°C) in July 1978, in Solano Bay (5.27°C) in March 1998 and Malpelo (3.64°C) in July 2015. A high correlation between SST and ONI in large part of study period, followed by a good correlation with PDO, was identified. The AO and SSTA have showed an inverse relationship in some periods. Solar Cycle has showed to be a modulator of behavior of SSTA in the selected stations. It was determined that extreme values of SST are related to the analyzed large scale oscillations.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

Recession and risk of suicide in Denmark during the 2009 global financial crisis: an ecological register-based study

2021 ◽  
pp. 140349482110132
Author(s):  
Agnieszka Konieczna ◽  
Sarah Grube Jakobsen ◽  
Christina Petrea Larsen ◽  
Erik Christiansen
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Time Series Analysis ◽  
Suicide Rate ◽  
Global Financial Crisis ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Prevention Programmes ◽  
The Impact

Aim: The aim of this study is to analyse the potential impact from the financial crisis (onset in 2009) on suicide rates in Denmark. The hypothesis is that the global financial crisis raised unemployment which leads to raising the suicide rate in Denmark and that the impact is most prominent in men. Method: This study used an ecological study design, including register data from 2001 until 2016 on unemployment, suicide, gender and calendar time which was analysed using Poisson regression models and interrupted time series analysis. Results: The correlation between unemployment and suicide rates was positive in the period and statistically significant for all, but at a moderate level. A dichotomised version of time (calendar year) showed a significant reduction in the suicide rate for women (incidence rate ratio 0.87, P=0.002). Interrupted time series analysis showed a significant decreasing trend for the overall suicide rate and for men in the pre-recession period, which in both cases stagnated after the onset of recession in 2009. The difference between the genders’ suicide rate changed significantly at the onset of recession, as the rate for men increased and the rate for women decreased. Discussion: The Danish social welfare model might have prevented social disintegration and suicide among unemployed, and suicide prevention programmes might have prevented deaths among unemployed and mentally ill individuals. Conclusions: We found some indications for gender-specific differences from the impact of the financial crises on the suicide rate. We recommend that men should be specifically targeted for appropriate prevention programmes during periods of economic downturn.

scholarly journals Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 3044
Author(s):  
Mingjie Liao ◽  
Rui Zhang ◽  
Jichao Lv ◽  
Bin Yu ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Ground Deformation ◽  
Dynamic Change ◽  
Deformation Monitoring ◽  
Chinese Loess Plateau ◽  
Ground Subsidence ◽  
Shanxi Province ◽  
Environment Change ◽  
Loess Area

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.

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