Journal articles on the topic 'Temporal heteroscedasticity'
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Sall’, M. A. "Climate risks: Temporal trends and heteroscedasticity." Russian Meteorology and Hydrology 40, no. 7 (July 2015): 489–94. http://dx.doi.org/10.3103/s1068373915070080.
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Zhang, Xiaolong. "Inventory control under temporal demand heteroscedasticity." European Journal of Operational Research 182, no. 1 (October 2007): 127–44. http://dx.doi.org/10.1016/j.ejor.2006.06.057.
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Fouladi, Seyyed Hamed, Ilangko Balasingham, Kimmo Kansanen, and Tor Audun Ramstad. "Blind Source Separation Using Temporal Correlation, Non-Gaussianity and Conditional Heteroscedasticity." IEEE Access 6 (2018): 25336–50. http://dx.doi.org/10.1109/access.2018.2823381.
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Zhang, Lyuou, Wen Zhou, and Haonan Wang. "A semiparametric latent factor model for large scale temporal data with heteroscedasticity." Journal of Multivariate Analysis 186 (November 2021): 104786. http://dx.doi.org/10.1016/j.jmva.2021.104786.
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Elshall, Ahmed S., Ming Ye, Guo-Yue Niu, and Greg A. Barron-Gafford. "Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy." Geoscientific Model Development 12, no. 5 (May 23, 2019): 2009–32. http://dx.doi.org/10.5194/gmd-12-2009-2019.
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Abstract. Bayesian inference of microbial soil respiration models is often based on the assumptions that the residuals are independent (i.e., no temporal or spatial correlation), identically distributed (i.e., Gaussian noise), and have constant variance (i.e., homoscedastic). In the presence of model discrepancy, as no model is perfect, this study shows that these assumptions are generally invalid in soil respiration modeling such that residuals have high temporal correlation, an increasing variance with increasing magnitude of CO2 efflux, and non-Gaussian distribution. Relaxing these three assumptions stepwise results in eight data models. Data models are the basis of formulating likelihood functions of Bayesian inference. This study presents a systematic and comprehensive investigation of the impacts of data model selection on Bayesian inference and predictive performance. We use three mechanistic soil respiration models with different levels of model fidelity (i.e., model discrepancy) with respect to the number of carbon pools and the explicit representations of soil moisture controls on carbon degradation; therefore, we have different levels of model complexity with respect to the number of model parameters. The study shows that data models have substantial impacts on Bayesian inference and predictive performance of the soil respiration models such that the following points are true: (i) the level of complexity of the best model is generally justified by the cross-validation results for different data models; (ii) not accounting for heteroscedasticity and autocorrelation might not necessarily result in biased parameter estimates or predictions, but will definitely underestimate uncertainty; (iii) using a non-Gaussian data model improves the parameter estimates and the predictive performance; and (iv) accounting for autocorrelation only or joint inversion of correlation and heteroscedasticity can be problematic and requires special treatment. Although the conclusions of this study are empirical, the analysis may provide insights for selecting appropriate data models for soil respiration modeling.
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Nagahara, Ryu, and Jean-Benoit Morin. "Sensor insole for measuring temporal variables and vertical force during sprinting." Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology 232, no. 4 (January 19, 2018): 369–74. http://dx.doi.org/10.1177/1754337117751730.
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Temporal variables and vertical ground reaction force have been used as measures characterizing sprinting. A recently developed wireless pressure sensor insole (sensor insole) could be useful for monitoring sprinting in terms of temporal variables and vertical ground reaction force during training sessions. The purpose of this study was to examine the concurrent validity of the sensor insole for measuring temporal and vertical force variables during sprinting. One athlete performed five 50-m sprints, and the step-to-step vertical ground reaction force and plantar pressure were simultaneously measured by a long-force platform system (reference device) and the sensor insole, respectively. The temporal and vertical ground reaction force variables were calculated using signals from both devices, and a comparison was made between values obtained with both devices for 125 steps analyzed. The percentage bias, 95% limits of agreement, and Bland–Altman plots showed low agreement with the reference device for all variables except for step frequency. For the vertical ground reaction force variables, the sensor insole underestimated the values (−18.9 to −48.3%) compared to the force platform. While support time and time to maximal vertical force from the foot strike were overestimated by the sensor insole (54.6 ± 8.0% and 94.2 ± 23.2%), flight time was underestimated (−48.2 ± 15.0%). Moreover, t-test revealed the significant difference in all variables between the sensor insole and force platform, except for step frequency. The bias for step frequency (0.4 ± 7.5%) was small. However, there was heteroscedasticity for all variables. The results from this study demonstrate that a wireless pressure sensor insole is generally not valid to measure the temporal and vertical force variables during sprinting. Thus, using the examined sensor insole for monitoring sprinting characteristics is not recommended at this time.
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Lei, Bolin, Boyu Zhang, and Yuping Song. "Volatility Forecasting for High-Frequency Financial Data Based on Web Search Index and Deep Learning Model." Mathematics 9, no. 4 (February 5, 2021): 320. http://dx.doi.org/10.3390/math9040320.
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The existing index system for volatility forecasting only focuses on asset return series or historical volatility, and the prediction model cannot effectively describe the highly complex and nonlinear characteristics of the stock market. In this study, we construct an investor attention factor through a Baidu search index of antecedent keywords, and then combine other trading information such as the trading volume, trend indicator, quote change rate, etc., as input indicators, and finally employ the deep learning model via temporal convolutional networks (TCN) to forecast the volatility under high-frequency financial data. We found that the prediction accuracy of the TCN model with investor attention is better than those of the TCN model without investor attention, the traditional econometric model as the generalized autoregressive conditional heteroscedasticity (GARCH), the heterogeneous autoregressive model of realized volatility (HAR-RV), autoregressive fractionally integrated moving average (ARFIMA) models, and the long short-term memory (LSTM) model with investor attention. Compared with the traditional econometric models, the multi-step prediction results for the TCN model remain robust. Our findings provide a more accurate and robust method for volatility forecasting for big data and enrich the index system of volatility forecasting.
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Souza, Eniuce Menezes de, Dário Sodré, Isabella Harumi Yonehara Noma, Cinthia Akemi Tanoshi, and Raissa Bocchi Pedroso. "Trend change estimation for interrupted time series with heteroscedastic and autocorrelated errors: application in syphilis occurrences in Brazil." Acta Scientiarum. Technology 44 (May 25, 2022): e59513. http://dx.doi.org/10.4025/actascitechnol.v44i1.59513.
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The impact evaluation of exogenous policies over time is of great importance in several areas. Unfortunately, an adequate time-series analysis has not always been taken into account in the literature, mainly in health problems. When regression models are used in the known interrupted time-series approach, the required error assumptions are in general neglected. Specifically, usual linear segmented regression (lmseg) models are not adequate when the errors have nonconstant variance and serial correlation. To instigate the correct use of intervention analysis, we present a simple approach extending a linear model with log-linear variance (lmvar) to estimate linear trend changes under heteroscedastic errors (lmsegvar). When the errors are autocorrelated, the Cochrane-Orcutt (CO) modification is implemented to correct the estimated parameters. As an application, we estimate the impact in temporal trend of the Brazilian Rede Mãe Paranaense (RMP) program in gestational syphilis occurrences in the state of Parana, Brazil. The comparison of the proposed linear segmented model (lmsegvar+CO) modeling both the average and variance, with the usual segmented linear model (lmseg), where just the average is modeled, shows the importance of taking heteroscedasticity and autocorrelation into account.
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Uddin, Mohammad Ahsan, ASM Maksud Kamal, Shamsuddin Shahid, and Eun-Sung Chung. "Volatility in Rainfall and Predictability of Droughts in Northwest Bangladesh." Sustainability 12, no. 23 (November 24, 2020): 9810. http://dx.doi.org/10.3390/su12239810.
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This study was conducted to evaluate the variability, trends, volatility, and transition patterns of rainfall in drought-prone northwest Bangladesh. Daily rainfall recorded at five stations for the period 1959–2018 were used for this purpose. Non-parametric tests of variability changes, a modified Mann–Kendall trend test, innovative trend analysis (ITA), a generalized autoregressive conditional heteroscedasticity (GARCH)–jump model, and a Markov chain (MC) were used to assess the variability changes, trends, volatility, and transitions in rainfall to understand the possibility of the persistence of droughts and their predictability. The results showed an overall decrease of variability in annual and seasonal rainfall, but an increase in mean pre-monsoon rainfall and a decrease in mean monsoon rainfall. This caused a decrease in pre-monsoon droughts, but few changes in monsoon droughts. The ITA and rainfall anomaly analysis revealed high temporal variability and, thus, rapid shifts in rainfall regimes, which were also supported by the volatility dynamics and time-varying jumps from the GARCH–jump model and the rapid changes in drought index from the MC analysis. Therefore, the lack of drought in recent years cannot be considered as an indicator of declining droughts in the region.
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García-Pinillos, Felipe, Diego Jaén-Carrillo, Victor Soto Hermoso, Pedro Latorre Román, Pedro Delgado, Cristian Martinez, Antonio Carton, and Luis Roche Seruendo. "Agreement Between Spatiotemporal Gait Parameters Measured by a Markerless Motion Capture System and Two Reference Systems—a Treadmill-Based Photoelectric Cell and High-Speed Video Analyses: Comparative Study." JMIR mHealth and uHealth 8, no. 10 (October 23, 2020): e19498. http://dx.doi.org/10.2196/19498.
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Background Markerless systems to capture body motion require no markers to be attached to the body, thereby improving clinical feasibility and testing time. However, the lack of markers might affect the accuracy of measurements. Objective This study aimed to determine the absolute reliability and concurrent validity of the Kinect system with MotionMetrix software for spatiotemporal variables during running at a comfortable velocity, by comparing data between the combination system and two widely used systems—OptoGait and high-speed video analysis at 1000 Hz. Methods In total, 25 runners followed a running protocol on a treadmill at a speed of 12 km/h. The Kinect+MotionMetrix combination measured spatiotemporal parameters during running (ie, contact time, flight time, step frequency, and step length), which were compared to those obtained from two reference systems. Results Regardless of the system, flight time had the highest coefficients of variation (OptoGait: 16.4%; video analysis: 17.3%; Kinect+MotionMetrix: 23.2%). The rest of the coefficients of variation reported were lower than 8.1%. Correlation analysis showed very high correlations (r>0.8; P<.001) and almost perfect associations (intraclass correlation coefficient>0.81) between systems for all the spatiotemporal parameters except contact time, which had lower values. Bland-Altman plots revealed smaller systematic biases and random errors for step frequency and step length and larger systematic biases and random errors for temporal parameters with the Kinect+MotionMetrix system as compared to OptoGait (difference: contact time +3.0%, flight time −7.9%) and high-speed video analysis at 1000 Hz (difference: contact time +4.2%, flight time −11.3%). Accordingly, heteroscedasticity was found between systems for temporal parameters (r2>0.1). Conclusions The results indicate that the Kinect+MotionMetrix combination slightly overestimates contact time and strongly underestimates flight time as compared to the OptoGait system and high-speed video analysis at 1000 Hz. However, it is a valid tool for measuring step frequency and step length when compared to reference systems. Future studies should determine the reliability of this system for determining temporal parameters.
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Follis, Jack L., and Dejian Lai. "Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models." Signals 1, no. 1 (June 2, 2020): 26–46. http://dx.doi.org/10.3390/signals1010003.
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Objective: To determine if there was a difference in the volatility characteristics of seizure and non-seizure onset channels in the intracranial electroencephalogram (EEG) in a patient with temporal lobe epilepsy. Methods: The half-life of volatility for the different EEG channels was determined using Autoregressive Moving Average–Generalized Autoregressive Conditional Heteroscedasticity (ARMA–GARCH) models; confidence intervals were constructed using the delta method and an asymptotic method for comparing the half-lives. Results: Clinically determined seizure onsets occurred over strip electrodes named RAST (Right Anterior Subtemporal) and RMST (Right Mid Subtemporal), at locations 2, 3 and 4, on the strip electrodes. The half-lives of volatility for two of the three seizure channels, RAST3 and RAST4, were found to be significantly lower the rest of the channels for six one-minute EEG segments prior to seizure onset and nine one-minute EEG segments of an awake state. The half-lives of volatility for RAST3 and RAST4 were not significantly different to the non-seizure channels for ten one-minute segments of sleep and ten one-minute segments of sleep-to-awake states. The estimates for the half-lives were consistent for randomly selected one-minute EEG segments. Conclusions: The use of GARCH models may be a useful tool in determining hidden properties in epileptiform EEGs that may lead to better understanding of the seizure generating process.
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Massa, Ricardo, and Gustavo Fondevila. "Police crackdowns in Mexico City." Policing: An International Journal 42, no. 5 (October 10, 2019): 798–813. http://dx.doi.org/10.1108/pijpsm-11-2018-0165.
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Purpose The purpose of this paper is to analyze the design and implementation of the police crackdown strategy employed in Mexico City and to discuss its limitations toward a medium-to-long-term reduction of crime rates for six types of robberies. Design/methodology/approach The present work employs generalized autoregressive conditional heteroscedasticity (GARCH) models to estimate the effect of police operations on the volatility of the rates of six types of robberies in Mexico City, as well as their persistence over time. Findings Results suggest that the concentration of policing in certain high-criminality spaces reduces crime rates in the immediate term; however, its permanence is contingent on policing design and behavioral characteristics of the targeted crime. Specifically, the Mexico City police crackdown strategy seems to be better suited for combating crimes of a “non-static” nature than those of a “static” nature. Research limitations/implications Due to the nature of the data used for this research, the performed analysis does not enable a precise determination of whether the crime rates respond to temporal or spatial displacement. Practical implications Considering the obtained results, a re-design of Mexico City’s police crackdown strategy is suggested for the sustained reduction of the number of reported cases of robberies of a static nature. Originality/value Despite their importance, few studies have measured the impact of police crackdowns on city-level crime rates and whether their effect is temporary or permanent. The present study proposes the use of GARCH models in order to integrate the study of this phenomenon into criminal time series models.
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Gerte, Raymond, Karthik C. Konduri, and Naveen Eluru. "Is There a Limit to Adoption of Dynamic Ridesharing Systems? Evidence from Analysis of Uber Demand Data from New York City." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 42 (July 21, 2018): 127–36. http://dx.doi.org/10.1177/0361198118788462.
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Recent technological advances have paved the way for new mobility alternatives within established transportation networks, including on-demand ride hailing/sharing (e.g., Uber, Lyft) and citywide bike sharing. Common across these innovative modes is a lack of direct ownership by the user; in each of these mobility offerings, a resource not owned by the end users’ is shared for fulfilling travel needs. This concept has flourished and is being hailed as a potential option for autonomous vehicle operation moving forward. However, substantial investigation into how new shared modes affect travel behaviors and integrate into existing transportation networks is lacking. This paper explores whether the growth in the adoption and usage of these modes is unbounded, or if there is a limit to their uptake. Recent trends and shifts in Uber demand usage from New York City were investigated to explore the hypothesis. Using publicly available data about Uber trips, temporal trends in the weekly demand for Uber were explored in the borough of Manhattan. A panel-based random effects model accounting for both heteroscedasticity and autocorrelation effects was estimated wherein weekly demand was expressed as a function of a variety of demographic, land use, and environmental factors. It was observed that demand appeared to initially increase after the introduction of Uber, but seemed to have stagnated and waned over time in heavily residential portions of the island, contradicting the observed macroscopic unbounded growth. The implications extend beyond already existing fully shared systems and also affect the planning of future mobility offerings.
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Yang, Han, Lihua Xiong, Qiumei Ma, Jun Xia, Jie Chen, and Chong-Yu Xu. "Utilizing Satellite Surface Soil Moisture Data in Calibrating a Distributed Hydrological Model Applied in Humid Regions Through a Multi-Objective Bayesian Hierarchical Framework." Remote Sensing 11, no. 11 (June 3, 2019): 1335. http://dx.doi.org/10.3390/rs11111335.
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The traditional calibration objective of hydrological models is to optimize streamflow simulations. To identify the value of satellite soil moisture data in calibrating hydrological models, a new objective of optimizing soil moisture simulations has been added to bring in satellite data. However, it leads to problems: (i) how to consider the trade-off between various objectives; (ii) how to consider the uncertainty these satellite data bring in. Among existing methods, the multi-objective Bayesian calibration framework has the potential to solve both problems but is more suitable for lumped models since it can only deal with constant variances (in time and space) of model residuals. In this study, to investigate the utilization of a soil moisture product from the Soil Moisture Active Passive (SMAP) satellite in calibrating a distributed hydrological model, the DEM (Digital Elevation Model) -based Distributed Rainfall-Runoff Model (DDRM), a multi-objective Bayesian hierarchical framework is employed in two humid catchments of southwestern China. This hierarchical framework is superior to the non-hierarchical framework when applied to distributed models since it considers the spatial and temporal residual heteroscedasticity of distributed model simulations. Taking the streamflow-based single objective calibration as the benchmark, results of adding satellite soil moisture data in calibration show that (i) there is less uncertainty in streamflow simulations and better performance of soil moisture simulations either in time and space; (ii) streamflow simulations are largely affected, while soil moisture simulations are slightly affected by weights of objectives. Overall, the introduction of satellite soil moisture data in addition to observed streamflow in calibration and putting more weights on the streamflow calibration objective lead to better hydrological performance. The multi-objective Bayesian hierarchical framework implemented here successfully provides insights into the value of satellite soil moisture data in distributed model calibration.
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Williamson, Emily R., and Christopher J. Sergeant. "Independent validation of downscaled climate estimates from a coastal Alaska watershed using local historical weather journals." PeerJ 9 (September 10, 2021): e12055. http://dx.doi.org/10.7717/peerj.12055.
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Downscaling coarse global and regional climate models allows researchers to access weather and climate data at finer temporal and spatial resolution, but there remains a need to compare these models with empirical data sources to assess model accuracy. Here, we validate a widely used software for generating North American downscaled climate data, ClimateNA, with a novel empirical data source, 20th century weather journals kept by Admiralty Island, Alaska homesteader, Allen Hasselborg. Using Hasselborg’s journals, we calculated monthly precipitation and monthly mean of the maximum daily air temperature across the years 1926 to 1954 and compared these to ClimateNA data generated from the Hasselborg homestead location and adjacent areas. To demonstrate the utility and potential implications of this validation for other disciplines such as hydrology, we used an established regression equation to generate time series of 95% low duration flow estimates for the month of August using mean annual precipitation from ClimateNA predictions and Hasselborg data. Across 279 months, we found strong correlation between modeled and observed measurements of monthly precipitation (ρ = 0.74) and monthly mean of the maximum daily air temperature (ρ = 0.98). Monthly precipitation residuals (calculated as ClimateNA data - Hasselborg data) generally demonstrated heteroscedasticity around zero, but a negative trend in residual values starting during the last decade of observations may have been due to a shift to the cold-phase Pacific Decadal Oscillation. Air temperature residuals demonstrated a consistent but small positive bias, with ClimateNA tending to overestimate air temperature relative to Hasselborg’s journals. The degree of correlation between weather patterns observed at the Hasselborg homestead site and ClimateNA data extracted from spatial grid cells across the region varied by wet and dry climate years. Monthly precipitation from both data sources tended to be more similar across a larger area during wet years (mean ρ across grid cells = 0.73) compared to dry years (mean ρ across grid cells = 0.65). The time series of annual 95% low duration flow estimates for the month of August generated using ClimateNA and Hasselborg data were moderately correlated (ρ = 0.55). Our analysis supports previous research in other regions which also found ClimateNA to be a robust source for past climate data estimates.
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Saima, T. "Geopolitics of International Relations, Ethnic Polarization and Internal Conflict." SocioEconomic Challenges 3, no. 4 (2019): 25–38. http://dx.doi.org/10.21272/sec.3(4).25-38.2019.
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Geostrategic position of a country not just creates opportunities in form of bilateral and multilateral collaborations, it may also pose stern long term concerns and spillover effects in terms of insecurity and conflict. Pakistan, if not a classic example, is a typical case of continually high geopolitics of international relations: its geostrategic location had been praised by international players during the cold war regime through financial assistance; it was encouraged to take part during the Russian invasion in Afghanistan in late 70’s; and was compelled to play the role of ‘front line state’ in the war against terrorism, in the aftermath of the 9/ 11 incidence, in 2001. Early attempts of establishing rebel groups based upon ethnic identity to fight in Afghanistan, while launching of ruthless military operations after 9/11 incidence, against same rebel groups who fought in Afghanistan during the Russian invasion, causing either undue leverage to specific ethnic minorities at one point in time or extreme repression at later stage of history. In order to pretest impact of geopolitics of International Relations upon conflict, and if the interplay of geopolitics with ‘ethnic polarization’ affected ‘internal conflict’, several econometric models have been estimated. Along with testing the impact of geopolitical importance and its interplay with ethnic polarization in distressing peace, other important propositions in estimated models include, how ‘external conflict’, ‘institutional efficacies’, and the ‘role of military in politics’, caused adversity of ‘Internal conflict’, in Pakistan. In order to ensure concurrent validity of econometric models, alternative regressands namely ratings of ‘Civil War’ and ‘Internal Conflict’ have been used. Keeping in view ordinal scaling of regressands, cautions in dealing with heteroscedasticity and potentially lagged impact of regressors, Ordered-probit, Ordered Logit, Quantile regression, Robust Regression, and Prais-Winsten models are estimated. Estimated models strongly approved the notion that ‘geopolitics of international relations’ and ‘geopolitics of International Relations’ in interaction with ‘Ethnic polarization’, have had a considerable and statistically significant temporal impact upon ‘internal conflict’ and rating of ‘civil war’, in context of Pakistan. Other significant factors that contributed to adversity of peace are ‘external conflict’, ‘role of military in politics’, ‘illegitimacy of the state actions’/ ‘institutional inefficacies’ and ‘religious polarization’. Keywords: geopolitics of international relations, war against terrorism, internal conflict, ethnic polarization, ordered-probit model, robust regression, Prais-Winsten regression.
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Lorenz, Christof, Tanja C. Portele, Patrick Laux, and Harald Kunstmann. "Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions." Earth System Science Data 13, no. 6 (June 15, 2021): 2701–22. http://dx.doi.org/10.5194/essd-13-2701-2021.
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Abstract. Seasonal forecasts have the potential to substantially improve water management particularly in water-scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium-Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface rerun of the latest ECMWF reanalysis ERA5. Thereby, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1∘. This is performed for example over four predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the São Francisco River (Brazil), the Tekeze–Atbara river and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo–Chira river (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts as well as remarkably reduced lead-dependent drift effects. But our analysis also shows that computing monthly averages from daily bias-corrected forecasts particularly during periods with strong temporal climate gradients or heteroscedasticity can lead to remaining biases especially in the lowest- and highest-lead forecasts. Our SEAS5 BCSD forecasts cover the whole (re-)forecast period from 1981 to 2019 and include bias-corrected and spatially disaggregated daily and monthly ensemble forecasts for precipitation, average, minimum, and maximum temperature as well as for shortwave radiation from the issue date to the next 215 d and 6 months, respectively. This sums up to more than 100 000 forecasted days for each of the 25 (until the year 2016) and 51 (from the year 2017) ensemble members and each of the five analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at https://doi.org/10.26050/WDCC/SaWaM_D01_SEAS5_BCSD (Domain D01, Karun Basin (Iran), Lorenz et al., 2020b), https://doi.org/10.26050/WDCC/SaWaM_D02_SEAS5_BCSD (Domain D02: São Francisco Basin (Brazil), Lorenz et al., 2020c), https://doi.org/10.26050/WDCC/SaWaM_D03_SEAS5_BCSD (Domain D03: basins of the Tekeze–Atbara and Blue Nile (Ethiopia, Eritrea, Sudan), Lorenz et al., 2020d), and https://doi.org/10.26050/WDCC/SaWaM_D04_SEAS5_BCSD (Domain D04: Catamayo–Chira Basin (Ecuador, Peru), Lorenz et al., 2020a). It is currently the first publicly available daily high-resolution seasonal forecast product that covers multiple regions and variables for such a long period. It hence provides a unique test bed for evaluating the performance of seasonal forecasts over semi-arid regions and as driving data for hydrological, ecosystem or climate impact models. Therefore, our forecasts provide a crucial contribution for the disaster preparedness and, finally, climate proofing of the regional water management in climatically sensitive regions.
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Rakshit, Debopam, Arkaprava Roy, Koushik Atta, Saju Adhikary, and Vishwanath. "Modeling Temporal Variation of Particulate Matter Concentration at Three Different Locations of Delhi." International Journal of Environment and Climate Change, August 31, 2022, 1831–39. http://dx.doi.org/10.9734/ijecc/2022/v12i1131191.
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Aims: To model the concentration variation of PM2.5 and PM10 in selected locations of Delhi.
Study Design: ARFIMA-GARCH model.
Place and Duration of Study: The study was conducted by using daily (24 hour interval) data of PM2.5 and PM10 concentration from three air quality monitoring stations of Delhi namely, Narela, Okhla Phase II and Pusa.
Methodology: The ARFIMA model is applied as the mean model and the GARCH model as the variance model.
Results: The selected series are stationary and exhibit the presence of long memory in the mean structure. Due to the presence of long memory in mean, the ARFIMA model is applied. The residual series have conditional heteroscedasticity. Hence, the GARCH model is applied as a variance model. The fitted models are validated using RMSE, MAE and MAPE.
Conclusion: The concentration variation of PM2.5 and PM10 followed long memory process in mean structure. ARFIMA-GARCH model satisfactorily explained the variation of concentration.