scholarly journals Comparing estimation techniques for temporal scaling in palaeoclimate time series

2021 ◽  
Vol 28 (3) ◽  
pp. 311-328
Author(s):  
Raphaël Hébert ◽  
Kira Rehfeld ◽  
Thomas Laepple
Keyword(s):  
Time Series ◽  
Structure Function ◽  
A Priori ◽  
Scaling Exponent ◽  
Traditional Methods ◽  
Regular Time ◽  
Linear Slope ◽  
Irregular Data ◽  
Multitaper Spectrum ◽  
Do So

Abstract. Characterizing the variability across timescales is important for understanding the underlying dynamics of the Earth system. It remains challenging to do so from palaeoclimate archives since they are more often than not irregular, and traditional methods for producing timescale-dependent estimates of variability, such as the classical periodogram and the multitaper spectrum, generally require regular time sampling. We have compared those traditional methods using interpolation with interpolation-free methods, namely the Lomb–Scargle periodogram and the first-order Haar structure function. The ability of those methods to produce timescale-dependent estimates of variability when applied to irregular data was evaluated in a comparative framework, using surrogate palaeo-proxy data generated with realistic sampling. The metric we chose to compare them is the scaling exponent, i.e. the linear slope in log-transformed coordinates, since it summarizes the behaviour of the variability across timescales. We found that, for scaling estimates in irregular time series, the interpolation-free methods are to be preferred over the methods requiring interpolation as they allow for the utilization of the information from shorter timescales which are particularly affected by the irregularity. In addition, our results suggest that the Haar structure function is the safer choice of interpolation-free method since the Lomb–Scargle periodogram is unreliable when the underlying process generating the time series is not stationary. Given that we cannot know a priori what kind of scaling behaviour is contained in a palaeoclimate time series, and that it is also possible that this changes as a function of timescale, it is a desirable characteristic for the method to handle both stationary and non-stationary cases alike.

scholarly journals Comparing estimation techniques for timescale-dependent scaling of climate variability in paleoclimate time series

2021 ◽  
Author(s):  
Raphaël Hébert ◽  
Kira Rehfeld ◽  
Thomas Laepple
Keyword(s):  
Structure Function ◽  
A Priori ◽  
Scaling Exponent ◽  
Traditional Methods ◽  
First Order ◽  
Regular Time ◽  
Linear Slope ◽  
Irregular Data ◽  
Multitaper Spectrum ◽  
Do So

Abstract. Characterizing the variability across timescales is important to understand the underlying dynamics of the Earth system. It remains challenging to do so from paleoclimate archives since they are more than often irregular and traditional methods to produce timescale-dependent estimates of variability such as the classical periodogram and the multitaper spectrum generally require regular time sampling. We have compared those traditional methods using interpolation with interpolation-free methods, namely the Lomb-Scargle periodogram and the first-order Haar structure function. The ability of those methods to produce timescale-dependent estimates of variability when applied to irregular data was evaluated in a comparative framework using surrogate paleo-proxy data generated with realistic sampling. The metric we chose to compare them is the scaling exponent, i.e. the linear slope in log-transformed coordinates, since it summarizes the behaviour of the variability across timescales. We found that for scaling estimates in irregular timeseries, the interpolation-free methods are to be preferred over the methods requiring interpolation as they allow for the utilization of the information from shorter timescale which are particularly affected by the irregularity. In addition, our results suggest that the Haar structure function is the safer choice of interpolation-free method since the Lomb-Scargle periodogram is unreliable when the underlying process generating the timeseries is not stationary. Given that we cannot know a priori what kind of scaling behaviour is contained in a paleoclimate timeseries, and that it is also possible that this changes as a function of timescale, it is a desirable characteristic for the method to handle both stationary and non-stationary cases alike.

scholarly journals Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

2021 ◽  
Vol 13 (10) ◽  
pp. 2006
Author(s):  
Jun Hu ◽  
Qiaoqiao Ge ◽  
Jihong Liu ◽  
Wenyan Yang ◽  
Zhigui Du ◽  
...  
Keyword(s):  
Time Series ◽  
Hypothesis Testing ◽  
Surface Deformation ◽  
A Priori ◽  
Least Square ◽  
Deformation Model ◽  
Phase Time ◽  
Testing Theory ◽  
Sbas Insar ◽  
Deformation Models

The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series.

Econometrics for Modelling Climate Change

2021 ◽  
Author(s):  
Jennifer L. Castle ◽  
David F. Hendry
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Econometric Modeling ◽  
Complex Data ◽  
Explanatory Variables ◽  
Stochastic Trends ◽  
Trend Breaks ◽  
Abrupt Shifts ◽  
Climate Time Series ◽  
Do So

Shared features of economic and climate time series imply that tools for empirically modeling nonstationary economic outcomes are also appropriate for studying many aspects of observational climate-change data. Greenhouse gas emissions, such as carbon dioxide, nitrous oxide, and methane, are a major cause of climate change as they cumulate in the atmosphere and reradiate the sun’s energy. As these emissions are currently mainly due to economic activity, economic and climate time series have commonalities, including considerable inertia, stochastic trends, and distributional shifts, and hence the same econometric modeling approaches can be applied to analyze both phenomena. Moreover, both disciplines lack complete knowledge of their respective data-generating processes (DGPs), so model search retaining viable theory but allowing for shifting distributions is important. Reliable modeling of both climate and economic-related time series requires finding an unknown DGP (or close approximation thereto) to represent multivariate evolving processes subject to abrupt shifts. Consequently, to ensure that DGP is nested within a much larger set of candidate determinants, model formulations to search over should comprise all potentially relevant variables, their dynamics, indicators for perturbing outliers, shifts, trend breaks, and nonlinear functions, while retaining well-established theoretical insights. Econometric modeling of climate-change data requires a sufficiently general model selection approach to handle all these aspects. Machine learning with multipath block searches commencing from very general specifications, usually with more candidate explanatory variables than observations, to discover well-specified and undominated models of the nonstationary processes under analysis, offers a rigorous route to analyzing such complex data. To do so requires applying appropriate indicator saturation estimators (ISEs), a class that includes impulse indicators for outliers, step indicators for location shifts, multiplicative indicators for parameter changes, and trend indicators for trend breaks. All ISEs entail more candidate variables than observations, often by a large margin when implementing combinations, yet can detect the impacts of shifts and policy interventions to avoid nonconstant parameters in models, as well as improve forecasts. To characterize nonstationary observational data, one must handle all substantively relevant features jointly: A failure to do so leads to nonconstant and mis-specified models and hence incorrect theory evaluation and policy analyses.

Methodological Naturalism and Reflexivity Requirement

2021 ◽  
Vol 12 (3) ◽  
pp. 311-330
Author(s):  
Hamed Bikaraan-Behesht ◽  
Keyword(s):  
Scientific Method ◽  
A Priori ◽  
Vicious Circle ◽  
Methodological Naturalism ◽  
Traditional Methods ◽  
Inductive Argument ◽  
Scientific Methods ◽  
Methodological Rule

Methodological naturalists regard scientific method as the only effective way of acquiring knowledge. Quite the contrary, traditional analytic philosophers reject employing scientific method in philosophy as illegitimate unless it is justified by the traditional methods. One of their attacks on methodological naturalism is the objection that it is either incoherent or viciously circular: any argument that may be offered for methodological naturalism either employs a priori methods or involves a vicious circle that ensues from employing the very method that the argument is aimed to show its credentials. The charge of circularity has also been brought against the naturalistic arguments for specific scientific methods; like the inductive argument for induction and the abductive argument for the inference to the best explanation. In this paper, I respond to the charge of circularity using a meta-methodological rule that I call ‘reflexivity requirement.’ Giving two examples of philosophical works, I illustrate how the requirement has already been considered to be necessary for self-referential theories. At the end, I put forward a meta-philosophical explanation of the naturalism-traditionalism debate over the legitimate method of philosophy.

Notes and Queries

1901 ◽  
Vol 47 (197) ◽  
pp. 362-362
Keyword(s):  
A Priori ◽  
Lower Type ◽  
Sense Of Smell ◽  
Do So

Dr. W. Watson (Edinburgh) writes in reference to Dr. Ireland's study of Nietzsche, that Nietzsche's acute sense of smell is very characteristic. He regards it as a reversion to a lower type. But specially Dr. Watson says, “The weakness of his sexual instinct is childlike. He seems to have combined the intellect of a man with the morality of a child. Does such a combination often accompany non-developed sexual instincts? It ought to do so a priori.”

scholarly journals The Tragic Mulatta and Storytelling in Their Eyes Were Watching God

2019 ◽  
Vol 26 ◽  
pp. 71-88
Author(s):  
Ana Belén Pérez García
Keyword(s):  
Twentieth Century ◽  
Zora Neale Hurston ◽  
A Priori ◽  
Female Identity ◽  
Female Character ◽  
Black Race ◽  
Antebellum Literature ◽  
Literary Character ◽  
White Person ◽  
Do So

The figure of the tragic mulatta placed its origin in antebellum literature and was extensively used in the literature of the nineteenth and twentieth century. Much has been written about this literary character in a time when the problem of miscegenation was at its highest point, and when studies established that races were inherently different, meaning that the black race was inferior to the white one. Many authors have made use of this trope for different purposes, and Zora Neale Hurston was one of them. In her novel Their Eyes Were Watching God, Hurston creates Janie, a mulatta that a priori follows all the characteristics of this type of female character who, however, breaks away from most of them. She overcomes all stereotypes and prejudices, those imposed on her because of her condition of interracial offspring, and is able to take charge of her own life and challenge all these impositions feeling closer to her blackness and celebrating and empowering her female identity. In this vein, storytelling becomes the liberating force that helps her do so. It will become the tool that will enable her to ignore the need of passing as a white person and provide her with the opportunity to connect with her real identity and so feel free and happy, breaking with the tragic destiny of mulatta characters. Keywords: storytelling, tragic mulatta, blackness, Hurston.  

scholarly journals Monitoring distributed computing beyond the traditional time-series histogram

2020 ◽  
Vol 245 ◽  
pp. 03036
Author(s):  
M S Doidge ◽  
P. A. Love ◽  
J Thornton
Keyword(s):  
Time Series ◽  
Distributed Computing ◽  
Real Time ◽  
A Priori ◽  
Time Data ◽  
Use Of Time ◽  
Computing Services ◽  
Monitoring Tools ◽  
Novel Approach ◽  
Current Monitoring

In this work we describe a novel approach to monitor the operation of distributed computing services. Current monitoring tools are dominated by the use of time-series histograms showing the evolution of various metrics. These can quickly overwhelm or confuse the viewer due to the large number of similar looking graphs. We propose a supplementary approach through the sonification of real-time data streamed directly from a variety of distributed computing services. The real-time nature of this method allows operations staff to quickly detect problems and identify that a problem is still ongoing, avoiding the case of investigating an issue a-priori when it may already have been resolved. In this paper we present details of the system architecture and provide a recipe for deployment suitable for both site and experiment teams.

scholarly journals Savaris et al (2021) erroneously interpreted their regressions

2021 ◽  
Author(s):  
Carlos Góes
Keyword(s):  
Time Series ◽  
Short Note ◽  
Mortality Rates ◽  
Specific Time ◽  
Weighted Averages ◽  
Staying At Home ◽  
Do So ◽  
At Home

Savaris et al. (2021) aim at "verifying if staying at home had an impact on mortality rates." This short note shows that the methodology they have applied in their paper does not allow them to do so. An estimated coefficient β≈0 does not imply that there is no association between the variables in either country. Rather, their pairwise difference regressions are computing coefficients that are weighted-averages of region-specific time series regressions, such that it is possible that the association is significant in both regions but their weighted-averages is close to zero. Therefore, the results do not back up the conclusions of the paper.

scholarly journals Time Series Analysis of Land Cover Change in Dry Mountains: Insights from the Tajik Pamirs

2021 ◽  
Vol 13 (19) ◽  
pp. 3951
Author(s):  
Kim André Vanselow ◽  
Harald Zandler ◽  
Cyrus Samimi
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Land Cover Change ◽  
Vegetation Index ◽  
Demographic Data ◽  
Mountainous Areas ◽  
Generalized Additive Mixed Models ◽  
Additive Mixed Models ◽  
Irregular Data

Greening and browning trends in vegetation have been observed in many regions of the world in recent decades. However, few studies focused on dry mountains. Here, we analyze trends of land cover change in the Western Pamirs, Tajikistan. We aim to gain a deeper understanding of these changes and thus improve remote sensing studies in dry mountainous areas. The study area is characterized by a complex set of attributes, making it a prime example for this purpose. We used generalized additive mixed models for the trend estimation of a 32-year Landsat time series (1988–2020) of the modified soil adjusted vegetation index, vegetation data, and environmental and socio-demographic data. With this approach, we were able to cope with the typical challenges that occur in the remote sensing analysis of dry and mountainous areas, including background noise and irregular data. We found that greening and browning trends coexist and that they vary according to the land cover class, topography, and geographical distribution. Greening was detected predominantly in agricultural and forestry areas, indicating direct anthropogenic drivers of change. At other sites, greening corresponds well with increasing temperature. Browning was frequently linked to disastrous events, which are promoted by increasing temperatures.

scholarly journals THE ERROR TERM IN THE HISTORY OF TIME SERIES ECONOMETRICS

2001 ◽  
Vol 17 (2) ◽  
pp. 424-450 ◽  
Author(s):  
Duo Qin ◽  
Christopher L. Gilbert
Keyword(s):  
Time Series ◽  
Rational Expectations ◽  
Time Series Data ◽  
A Priori ◽  
Theoretical Models ◽  
Series Data ◽  
London School ◽  
Time Series Econometrics ◽  
Cowles Commission ◽  
Modeling Strategy

We argue that many methodological confusions in time-series econometrics may be seen as arising out of ambivalence or confusion about the error terms. Relationships between macroeconomic time series are inexact, and, inevitably, the early econometricians found that any estimated relationship would only fit with errors. Slutsky interpreted these errors as shocks that constitute the motive force behind business cycles. Frisch tried to dissect the errors further into two parts: stimuli, which are analogous to shocks, and nuisance aberrations. However, he failed to provide a statistical framework to make this distinction operational. Haavelmo, and subsequent researchers at the Cowles Commission, saw errors in equations as providing the statistical foundations for econometric models and required that they conform to a priori distributional assumptions specified in structural models of the general equilibrium type, later known as simultaneous-equations models. Because theoretical models were at that time mostly static, the structural modeling strategy relegated the dynamics in time-series data frequently to nuisance, atheoretical complications. Revival of the shock interpretation in theoretical models came about through the rational expectations movement and development of the vector autoregression modeling approach. The so-called London School of Economics dynamic specification approach decomposes the dynamics of the modeled variable into three parts: short-run shocks, disequilibrium shocks, and innovative residuals, with only the first two of these sustaining an economic interpretation.

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