scholarly journals Sentinel-2 Time Series Analysis for Identification of Underutilized Land in Europe

2021 ◽  
Vol 13 (23) ◽  
pp. 4920
Author(s):  
Carina Sobe ◽  
Manuela Hirschmugl ◽  
Andreas Wimmer
Keyword(s):  
Time Series ◽  
Renewable Energy Sources ◽  
Model Function ◽  
Time Series Modelling ◽  
Geographical Regions ◽  
Advanced Analysis ◽  
Improved Methods ◽  
Biomass For Energy ◽  
Sentinel 2

Biomass and bioenergy play a central role in Europe’s Green Transition. Currently, biomass is representing half of the renewable energy sources used. While the role of renewables in the energy mix is undisputed, there have been many controversial discussions on the use of biomass for energy due to the “food versus fuel” debate. Using previously underutilized lands for bioenergy is one possibility to prevent this discussion. This study supports the attempts to increase biomass for bioenergy through the provision of improved methods to identify underutilized lands in Europe. We employ advanced analysis methods based on time series modelling using Sentinel-2 (S2) data from 2017 to 2019 in order to distinguish utilized from underutilized land in twelve study areas in different bio-geographical regions (BGR) across Europe. The calculated parameters of the computed model function combined with temporal statistics were used to train a random forest classifier (RF). The achieved overall accuracies (OA) per study area vary between 80.25 and 96.76%, with confidence intervals (CI) ranging between 1.77% and 6.28% at a 95% confidence level. All in all, nearly 500,000 ha of underutilized land potentially available for agricultural bioenergy production were identified in this study, with the greatest amount mapped in Eastern Europe.

TIME SERIES MODELLING OF EPIDEMICS: LEADING INDICATORS, CONTROL GROUPS AND POLICY ASSESSMENT

2021 ◽  
Vol 257 ◽  
pp. 83-100
Author(s):  
Andrew Harvey
Keyword(s):  
Time Series ◽  
Data Science ◽  
Leading Indicators ◽  
Control Groups ◽  
Balanced Growth ◽  
Time Series Modelling ◽  
Key Variables ◽  
The Uk ◽  
The Relationship

This article shows how new time series models can be used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020, Harvard Data Science Review, Special Issue 1—COVID-19, https://hdsr.mitpress.mit.edu/pub/ozgjx0yn) is extended to model the relationship between two or more series and the role of common trends is discussed. Data on daily deaths from COVID-19 in Italy and the UK provides an example of leading indicators when there is a balanced growth. When growth is not balanced, the model can be extended by including a non-stationary component in one of the series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden’s soft lockdown coronavirus policy in the spring of 2020.

The role of parental self-regulation and household chaos in parent-toddler interactions: A time-series study.

2020 ◽  
Author(s):  
Sanne B. Geeraerts ◽  
Joyce Endendijk ◽  
Kirby Deater-Deckard ◽  
Jorg Huijding ◽  
Marike H. F. Deutz ◽  
...  
Keyword(s):  
Time Series ◽  
Self Regulation ◽  
Household Chaos ◽  
Time Series Study ◽  
Series Study

scholarly journals Using advanced analysis of multifocal visual-evoked potentials to evaluate the risk of clinical progression in patients with radiologically isolated syndrome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
J. M. Miguel ◽  
M. Roldán ◽  
C. Pérez-Rico ◽  
M. Ortiz ◽  
L. Boquete ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Area Under The Curve ◽  
Multifocal Visual Evoked Potentials ◽  
Radiologically Isolated Syndrome ◽  
Advanced Analysis ◽  
The Time Domain ◽  
Visual Evoked

AbstractThis study aimed to assess the role of multifocal visual-evoked potentials (mfVEPs) as a guiding factor for clinical conversion of radiologically isolated syndrome (RIS). We longitudinally followed a cohort of 15 patients diagnosed with RIS. All subjects underwent thorough ophthalmological, neurological and imaging examinations. The mfVEP signals were analysed to obtain features in the time domain (SNRmin: amplitude, Latmax: monocular latency) and in the continuous wavelet transform (CWT) domain (bmax: instant in which the CWT function maximum appears, Nmax: number of CWT function maximums). The best features were used as inputs to a RUSBoost boosting-based sampling algorithm to improve the mfVEP diagnostic performance. Five of the 15 patients developed an objective clinical symptom consistent with an inflammatory demyelinating central nervous system syndrome during follow-up (mean time: 13.40 months). The (SNRmin) variable decreased significantly in the group that converted (2.74 ± 0.92 vs. 4.07 ± 0.95, p = 0.01). Similarly, the (bmax) feature increased significantly in RIS patients who converted (169.44 ± 24.81 vs. 139.03 ± 11.95 (ms), p = 0.02). The area under the curve analysis produced SNRmin and bmax values of 0.92 and 0.88, respectively. These results provide a set of new mfVEP features that can be potentially useful for predicting prognosis in RIS patients.

scholarly journals The Role of Assumptions in Ohlson Model Performance: Lessons for Improving Equity-Value Modeling

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 513
Author(s):  
Olga Fullana ◽  
Mariano González ◽  
David Toscano
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Listed Firms ◽  
Valuation Model ◽  
Residual Income Valuation ◽  
Equity Value ◽  
Short Run ◽  
Time Series Approach ◽  
Linear Information

In this paper, we test whether the short-run econometric conditions for the basic assumptions of the Ohlson valuation model hold, and then we relate these results with the fulfillment of the short-run econometric conditions for this model to be effective. Better future modeling motivated us to analyze to what extent the assumptions involved in this seminal model are not good enough approximations to solve the firm valuation problem, causing poor model performance. The model is based on the well-known dividend discount model and the residual income valuation model, and it adds a linear information model, which is a time series model by nature. Therefore, we adopt the time series approach. In the presence of non-stationary variables, we focus our research on US-listed firms for which more than forty years of data with the required cointegration properties to use error correction models are available. The results show that the clean surplus relation assumption has no impact on model performance, while the unbiased accounting property assumption has an important effect on it. The results also emphasize the uselessness of forcing valuation models to match the value displacement property of dividends.

scholarly journals Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

2021 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Miguel Ángel Ruiz Reina
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prognostic Models ◽  
Series Data ◽  
Economic Agents ◽  
Time Series Modelling ◽  
Simulation And Training ◽  
Uncertainty Method ◽  
And Training ◽  
Hotel Accommodation

In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the high descriptive capacity of the model allow economic agents to make efficient decisions.

scholarly journals Calibrating the CreditRisk+ Model at Different Time Scales and in Presence of Temporal Autocorrelation †

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1679
Author(s):  
Jacopo Giacomelli ◽  
Luca Passalacqua
Keyword(s):  
Time Series ◽  
Default Risk ◽  
Real Life ◽  
Statistical Error ◽  
Real Data ◽  
Sampling Period ◽  
Calibration Procedure ◽  
Industry Standards ◽  
Default Rate

The CreditRisk+ model is one of the industry standards for the valuation of default risk in credit loans portfolios. The calibration of CreditRisk+ requires, inter alia, the specification of the parameters describing the structure of dependence among default events. This work addresses the calibration of these parameters. In particular, we study the dependence of the calibration procedure on the sampling period of the default rate time series, that might be different from the time horizon onto which the model is used for forecasting, as it is often the case in real life applications. The case of autocorrelated time series and the role of the statistical error as a function of the time series period are also discussed. The findings of the proposed calibration technique are illustrated with the support of an application to real data.

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