Bibliografie tematiche / Time series aggregation

Letteratura scientifica selezionata sul tema "Time series aggregation"

Autore: Grafiati

Pubblicato: 1 giugno 2024

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Articoli di riviste sul tema "Time series aggregation":

Rossana, Robert J., e John J. Seater. "Temporal Aggregation and Economic Time Series". Journal of Business & Economic Statistics 13, n. 4 (ottobre 1995): 441. http://dx.doi.org/10.2307/1392389.

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Rossana, Robert J., e John J. Seater. "Temporal Aggregation and Economic Time Series". Journal of Business & Economic Statistics 13, n. 4 (ottobre 1995): 441–51. http://dx.doi.org/10.1080/07350015.1995.10524618.

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Brännäs, Kurt, e Henry Ohlsson. "Asymmetric Time Series and Temporal Aggregation". Review of Economics and Statistics 81, n. 2 (maggio 1999): 341–44. http://dx.doi.org/10.1162/003465399558120.

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Yager, Ronald R. "Time Series Smoothing and OWA Aggregation". IEEE Transactions on Fuzzy Systems 16, n. 4 (agosto 2008): 994–1007. http://dx.doi.org/10.1109/tfuzz.2008.917299.

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de Jong, R., e S. de Bruin. "Time series of vegetation indices and the modifiable temporal unit problem". Biogeosciences Discussions 8, n. 4 (24 agosto 2011): 8545–61. http://dx.doi.org/10.5194/bgd-8-8545-2011.

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Abstract. Time series of vegetation indices (VI) derived from satellite imagery provide a consistent monitoring system for terrestrial plant systems. They enable detection and quantification of gradual changes within the time frame covered, which are of crucial importance in global change studies, for example. However, VI time series typically contain a strong seasonal signal which complicates change detection. Commonly, trends are quantified using linear regression methods, while the effect of serial autocorrelation is remediated by temporal aggregation over bins having a fixed width. Aggregating the data in this way produces temporal units which are modifiable. Analogous to the well-known Modifiable Area Unit Problem (MAUP), the way in which these temporal units are defined may influence the fitted model parameters and therefore the amount of change detected. This paper illustrates the effect of this Modifiable Temporal Unit Problem (MTUP) on a synthetic data set and a real VI data set. Large variation in detected changes was found for aggregation over bins that mismatched full lengths of vegetative cycles, which demonstrates that aperiodicity in the data may influence model results. Using 26 yr of VI data and aggregation over full-length periods, deviations in VI gains of less than 1 % were found for annual periods, while deviations (with respect to seasonally adjusted data) increased up to 24 % for aggregation windows of 5 yr. This demonstrates that temporal aggregation needs to be carried out with care in order to avoid spurious model results.

Celov, Dmitrij, e Remigijus Leipus. "Time series aggregation, disaggregation and long memory". Lietuvos matematikos rinkinys 46 (21 settembre 2023): 255–62. http://dx.doi.org/10.15388/lmr.2006.30723.

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Large-scale aggregation and its inverse, disaggregation, problems are important in many fields of studies like macroeconomics, astronomy, hydrology and sociology. It was shown in Granger (1980) that a certain aggregation of random coefficient AR(1) models can lead to long memory output. Dacunha-Castelle and Oppenheim (2001) explored the topic further, answering when and if a predefined long memory process could be obtained as the result of aggregation of a specific class of individual processes. In this paper, the disaggregation scheme of Leipus et al. (2006) is briefly discussed. Then disaggregation into AR(1) is analyzed further, resulting in a theorem that helps, under corresponding assumptions, to construct a mixture density for a given aggregated by AR(1) scheme process. Finally the theorem is illustrated by FARUMA mixture densityÆs example.

Zhu, Ye, Yongjian Fu e Huirong Fu. "Preserving Privacy in Time Series Data Mining". International Journal of Data Warehousing and Mining 7, n. 4 (ottobre 2011): 64–85. http://dx.doi.org/10.4018/jdwm.2011100104.

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Time series data mining poses new challenges to privacy. Through extensive experiments, the authors find that existing privacy-preserving techniques such as aggregation and adding random noise are insufficient due to privacy attacks such as data flow separation attack. This paper also presents a general model for publishing and mining time series data and its privacy issues. Based on the model, a spectrum of privacy preserving methods is proposed. For each method, effects on classification accuracy, aggregation error, and privacy leak are studied. Experiments are conducted to evaluate the performance of the methods. The results show that the methods can effectively preserve privacy without losing much classification accuracy and within a specified limit of aggregation error.

Beran, Jan, Haiyan Liu e Sucharita Ghosh. "On aggregation of strongly dependent time series". Scandinavian Journal of Statistics 47, n. 3 (13 dicembre 2019): 690–710. http://dx.doi.org/10.1111/sjos.12421.

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Kim, Hung Soo, Yong Nam Yoon, Gyu-Sei Yi e Taegyun Kim. "Effect of aggregation on chaotic time series". KSCE Journal of Civil Engineering 4, n. 4 (dicembre 2000): 219–26. http://dx.doi.org/10.1007/bf02823969.

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Chipman, J., e P. Winker. "Optimal aggregation of linear time series models". Computational Statistics & Data Analysis 49, n. 2 (aprile 2005): 311–31. http://dx.doi.org/10.1016/j.csda.2004.05.015.

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Tesi sul tema "Time series aggregation":

Tripodis, Georgios. "Heterogeneity and aggregation in seasonal time series". Thesis, London School of Economics and Political Science (University of London), 2007. http://etheses.lse.ac.uk/2933/.

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Seasonality is an important part of many real time series. While issues of seasonal heteroscedasticity and aggregation have been a cause of concern for data users, there has not been a great deal of theoretical research in this area. This thesis concentrates on these two issues. We consider seasonal time series with single season heteroscedasticity. We show that when only one month has different variability from others there are constraints on the seasonal models that can be used. We show that both the dummy and the trigonometric models are not effective in modelling seasonal series with this type of variability. We suggest two models that permit single season heteroscedasticity as a special case. We show that seasonal heteroscedasticity gives rise to periodic autocorrelation function. We propose a new class, called periodic structural time series models (PSTSM) to deal with such periodicities. We show that PSTSM have correlation structure equivalent to that of a periodic integrated moving average (PIMA) process. In a comparison of forecast performance for a set of quarterly macroeconomic series, PSTSM outperform periodic autoregressive (PAR) models both within and out of sample. We also consider the problem of contemporaneous aggregation of time series using the structural time series framework. We consider the conditions of identifiability for the aggregate series. We show that the identifiability of the models for the component series is not sufficient for the identifiability of the model for the aggregate series. We also consider the case where there is no estimation error as well as the case of modeling an unknown process. For the case of the unknown process we provide recursions based on the Kalman filter that give the asymptotic variance of the estimated parameters.

Lin, Shu-Chin. "Aggregation and time series implications of state-dependent consumption /". free to MU campus, to others for purchase, 1996. http://wwwlib.umi.com/cr/mo/fullcit?p9737881.

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Sariaslan, Nazli. "The Effect Of Temporal Aggregation On Univariate Time Series Analysis". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612528/index.pdf.

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Most of the time series are constructed by some kind of aggregation and temporal aggregation that can be defined as aggregation over consecutive time periods. Temporal aggregation takes an important role in time series analysis since the choice of time unit clearly influences the type of model and forecast results. A totally different time series model can be fitted on the same variable over different time periods. In this thesis, the effect of temporal aggregation on univariate time series models is studied by considering modeling and forecasting procedure via a simulation study and an application based on a southern oscillation data set. Simulation study shows how the model, mean square forecast error and estimated parameters change when temporally aggregated data is used for different orders of aggregation and sample sizes. Furthermore, the effect of temporal aggregation is also demonstrated through southern oscillation data set for different orders of aggregation. It is observed that the effect of temporal aggregation should be taken into account for data analysis since temporal aggregation can give rise to misleading results and inferences.

Gehman, Andrew J. "The Effects of Spatial Aggregation on Spatial Time Series Modeling and Forecasting". Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/382669.

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Abstract (sommario):

Statistics
Ph.D.
Spatio-temporal data analysis involves modeling a variable observed at different locations over time. A key component of space-time modeling is determining the spatial scale of the data. This dissertation addresses the following three questions: 1) How does spatial aggregation impact the properties of the variable and its model? 2) What spatial scale of the data produces more accurate forecasts of the aggregate variable? 3) What properties lead to the smallest information loss due to spatial aggregation? Answers to these questions involve a thorough examination of two common space-time models: the STARMA and GSTARMA models. These results are helpful to researchers seeking to understand the impact of spatial aggregation on temporal and spatial correlation as well as to modelers interested in determining a spatial scale for the data. Two data examples are included to illustrate the findings, and they concern states' annual labor force totals and monthly burglary counts for police districts in the city of Philadelphia.
Temple University--Theses

Kim, Hang. "TIME SERIES BLOCK BOOTSTRAP APPLICATION AND EFFECT OF AGGREGATION AND SYSTEMATIC SAMPLING". Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/490644.

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Abstract (sommario):

Statistics
Ph.D.
In this dissertation, we review the basic properties of the bootstrap and time series application. Then we apply parametric bootstrap on three simulated normal i.i.d. samples and nonparametric bootstrap on four real life financial returns. Among the time series bootstrap methods, we look into the specific method called block bootstrap and investigate the block length consideration to properly select a suitable block size for AR(1) model. We propose a new rule of blocking named as Combinatorially-Augmented Block Bootstrap(CABB). We compare the existing block bootstrap and CABB method using the simulated i.i.d. samples, AR(1) time series, and the real life examples. Both methods perform equally well in estimating AR(1) coefficients. CABB produces a smaller standard deviation based on our simulated and empirical studies. We study two procedures of collecting time series, (i) aggregation of a flow variable and (ii) systematic sampling of a stock variable. In these two procedures, we derive theorems that calculate exact equations for $m$ aggregated and $m^{th}$ systematically sampled series of the original AR(1) model. We evaluate the performance of block bootstrap estimation of the parameters of ARMA(1,1) and AR(1) model using aggregated and systematically sampled series. Simulation and real data analyses show that in some cases, the performance of the estimation based on the block bootstrap method for the MA(1) parameter of the ARMA(1,1) model in aggregated series is better than the one without using bootstrap. In an extreme case of stock price movement, which is close to a random walk, the block bootstrap estimate using systematically sampled series is closer to the true parameter, defined as the parameter calculated by the theorem. Specifically, the block bootstrap estimate of the parameter of AR(1) model using the systematically sampled series is closer to phi(n) than that based on the MLE for the AR(1) model. Future research problems include theoretical investigation of CABB, effectiveness of block bootstrap in other time series analyses such as nonlinear or VAR.
Temple University--Theses

APRAEZ, CESAR DAVID REVELO. "A HYBRID NEURO- EVOLUTIONARY APPROACH FOR DYNAMIC WEIGHTED AGGREGATION OF TIME SERIES FORECASTERS". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36950@1.

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Abstract (sommario):

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Estudos empíricos na área de séries temporais indicam que combinar modelos preditivos, originados a partir de diferentes técnicas de modelagem, levam a previsões consensuais superiores, em termos de acurácia, às previsões individuais dos modelos envolvidos na combinação. No presente trabalho é apresentada uma metodologia de combinação convexa de modelos estatísticos de previsão, cujo sucesso depende da forma como os pesos de combinação de cada modelo são estimados. Uma Rede Neural Artificial Perceptron Multi-camada (Multilayer Perceptron - MLP) é utilizada para gerar dinamicamente vetores de pesos ao longo do horizonte de previsão, sendo estes dependentes da contribuição individual de cada previsor observada nos dados históricos da série. O ajuste dos parâmetros da rede MLP é efetuado através de um algoritmo de treinamento híbrido, que integra técnicas de busca global, baseadas em computação evolucionária, junto com o algoritmo de busca local backpropagation, de modo a otimizar de forma simultânea tanto os pesos quanto a arquitetura da rede, visando, assim, a gerar de forma automática um modelo de ponderação dinâmica de previsores de alto desempenho. O modelo proposto, batizado de Neural Expert Weighting - Genetic Algorithm (NEW-GA), foi avaliado em diversos experimentos comparativos com outros modelos de ponderação de previsores, assim como também com os modelos individuais envolvidos na combinação, contemplando 15 séries temporais divididas em dois estudos de casos: séries de derivados de petróleo e séries da versão reduzida da competição NN3, uma competição entre metodologias de previsão, com maior ênfase nos modelos baseados em Redes Neurais. Os resultados demonstraram o potencial do NEWGA em fornecer modelos acurados de previsão de séries temporais.
Empirical studies on time series indicate that the combination of forecasting models, generated from different modeling techniques, leads to higher consen+sus forecasts, in terms of accuracy, than the forecasts of individual models involved in the combination scheme. In this work, we present a methodology for convex combination of statistical forecasting models, whose success depends on how the combination weights of each model are estimated. An Artificial Neural Network Multilayer Perceptron (MLP) is used to generate dynamically weighting vectors over the forecast horizon, being dependent on the individual contribution of each forecaster observed over historical data series. The MLP network parameters are adjusted via a hybrid training algorithm that integrates global search techniques, based on evolutionary computation, along with the local search algorithm backpropagation, in order to optimize simultaneously both weights and network architecture. This approach aims to automatically generate a dynamic weighted forecast aggregation model with high performance. The proposed model, called Neural Expert Weighting - Genetic Algorithm (NEW-GA), was com- pared with other forecaster combination models, as well as with the individual models involved in the combination scheme, comprising 15 time series divided into two case studies: Petroleum Products and the reduced set of NN3 forecasting competition, a competition between forecasting methodologies, with greater emphasis on models based on neural networks. The results obtained demonstrated the potential of NEW-GA in providing accurate models for time series forecasting.

Weiss, Christoph. "Essays in hierarchical time series forecasting and forecast combination". Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274757.

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This dissertation comprises of three original contributions to empirical forecasting research. Chapter 1 introduces the dissertation. Chapter 2 contributes to the literature on hierarchical time series (HTS) modelling by proposing a disaggregated forecasting system for both inflation rate and its volatility. Using monthly data that underlies the Retail Prices Index for the UK, we analyse the dynamics of the inflation process. We examine patterns in the time-varying covariation among product-level inflation rates that aggregate up to industry-level inflation rates that in turn aggregate up to the overall inflation rate. The aggregate inflation volatility closely tracks the time path of this covariation, which is seen to be driven primarily by the variances of common shocks shared by all products, and by the covariances between idiosyncratic product-level shocks. We formulate a forecasting system that comprises of models for mean inflation rate and its variance, and exploit the index structure of the aggregate inflation rate using the HTS framework. Using a dynamic model selection approach to forecasting, we obtain forecasts that are between 9 and 155 % more accurate than a SARIMA-GARCH(1,1) for the aggregate inflation volatility. Chapter 3 is on improving forecasts using forecast combinations. The paper documents the software implementation of the open source R package for forecast combination that we coded and published on the official R package depository, CRAN. The GeomComb package is the only R package that covers a wide range of different popular forecast combination methods. We implement techniques from 3 broad categories: (a) simple non-parametric methods, (b) regression-based methods, and (c) geometric (eigenvector) methods, allowing for static or dynamic estimation of each approach. Using S3 classes/methods in R, the package provides a user-friendly environment for applied forecasting, implementing solutions for typical issues related to forecast combination (multicollinearity, missing values, etc.), criterion-based optimisation for several parametric methods, and post-fit functions to rationalise and visualise estimation results. The package has been listed in the official R Task Views for Time Series Analysis and for Official Statistics. The brief empirical application in the paper illustrates the package’s functionality by estimating forecast combination techniques for monthly UK electricity supply. Chapter 4 introduces HTS forecasting and forecast combination to a healthcare staffing context. A slowdown of healthcare budget growth in the UK that does not keep pace with growth of demand for hospital services made efficient cost planning increasingly crucial for hospitals, in particular for staff which accounts for more than half of hospitals’ expenses. This is facilitated by accurate forecasts of patient census and churn. Using a dataset of more than 3 million observations from a large UK hospital, we show how HTS forecasting can improve forecast accuracy by using information at different levels of the hospital hierarchy (aggregate, emergency/electives, divisions, specialties), compared to the naïve benchmark: the seasonal random walk model applied to the aggregate. We show that forecast combination can improve accuracy even more in some cases, and leads to lower forecast error variance (decreasing forecasting risk). We propose a comprehensive parametric approach to use forecasts in a nurse staffing model that has the aim of minimising cost while satisfying that the care requirements (e.g. nurse hours per patient day thresholds) are met.

Doell, Christoph [Verfasser]. "Methods for Multivariate Time-Series Classification on Brain Data : Aggregation, Stratification and Neural Network Models / Christoph Doell". Konstanz : KOPS Universität Konstanz, 2021. http://d-nb.info/1232176648/34.

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Bahl, Björn [Verfasser], André [Akademischer Betreuer] Bardow e Francois [Akademischer Betreuer] Marechal. "Optimization-based synthesis of large-scale energy systems by time-series aggregation / Björn Bahl ; André Bardow, Francois Marechal". Aachen : Universitätsbibliothek der RWTH Aachen, 2018. http://d-nb.info/1186069260/34.

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Lee, Bu Hyoung. "The use of temporally aggregated data on detecting a structural change of a time series process". Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/375511.

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Abstract (sommario):

Statistics
Ph.D.
A time series process can be influenced by an interruptive event which starts at a certain time point and so a structural break in either mean or variance may occur before and after the event time. However, the traditional statistical tests of two independent samples, such as the t-test for a mean difference and the F-test for a variance difference, cannot be directly used for detecting the structural breaks because it is almost certainly impossible that two random samples exist in a time series. As alternative methods, the likelihood ratio (LR) test for a mean change and the cumulative sum (CUSUM) of squares test for a variance change have been widely employed in literature. Another point of interest is temporal aggregation in a time series. Most published time series data are temporally aggregated from the original observations of a small time unit to the cumulative records of a large time unit. However, it is known that temporal aggregation has substantial effects on process properties because it transforms a high frequency nonaggregate process into a low frequency aggregate process. In this research, we investigate the effects of temporal aggregation on the LR test and the CUSUM test, through the ARIMA model transformation. First, we derive the proper transformation of ARIMA model orders and parameters when a time series is temporally aggregated. For the LR test for a mean change, its test statistic is associated with model parameters and errors. The parameters and errors in the statistic should be changed when an AR(p) process transforms upon the mth order temporal aggregation to an ARMA(P,Q) process. Using the property, we propose a modified LR test when a time series is aggregated. Through Monte Carlo simulations and empirical examples, we show that the aggregation leads the null distribution of the modified LR test statistic being shifted to the left. Hence, the test power increases as the order of aggregation increases. For the CUSUM test for a variance change, we show that two aggregation terms will appear in the test statistic and have negative effects on test results when an ARIMA(p,d,q) process transforms upon the mth order temporal aggregation to an ARIMA(P,d,Q) process. Then, we propose a modified CUSUM test to control the terms which are interpreted as the aggregation effects. Through Monte Carlo simulations and empirical examples, the modified CUSUM test shows better performance and higher test powers to detect a variance change in an aggregated time series than the original CUSUM test.
Temple University--Theses

Più fonti

Libri sul tema "Time series aggregation":

Zaffaroni, Paolo. Contemporaneous aggregation of GARCH processes. Roma: Banca d'Italia, 2002.

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Chambers, Marcus J. Long memory and aggregation in macroeconomic time series. Colchester: Essex University, Department of Economics, 1995.

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Lippi, Marco. Aggregation of simple linear dynamics: Exact asymptotic results. London: Suntory Centre, 1998.

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Urga, Giovanni. Panel data vs. time series regression analysis: An aggregation issue : a comparison using labour demand functions. London: London University, Queen Mary and Westfield College, Department of Economics, 1993.

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Lütkepohl, Helmut. Forecasting Aggregated Vector ARMA Processes. Berlin: Springer-Verlag, 1987.

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Pevehouse, Jon, e Jason D. Brozek. Time‐Series Analysis. A cura di Janet M. Box-Steffensmeier, Henry E. Brady e David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0019.

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Abstract (sommario):

This article discusses time-series methods such as simple time-series regressions, ARIMA models, vector autoregression (VAR) models, and unit root and error correction models (ECM). It specifically presents a brief history of time-series analysis before moving to a review of the basic time-series model. It then describes the stationary models in univariate and multivariate analyses. The nonstationary models of each type are addressed. In addition, various issues regarding the analysis of time series including data aggregation and temporal stability are considered. Before concluding, the article briefly reports the time-series techniques in the context of panel data. In general, time-series analysis can help improve the understanding of the political world.

Fazekas, Mihály, Luciana Cingolani e Bence Tóth. Innovations in Objectively Measuring Corruption in Public Procurement. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198817062.003.0007.

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While there is continued interest in measuring governance, disagreement on how best to do so has only grown over time. To provide pointers at innovative and rigorous indicator building, this chapter documents innovations in measuring a particularly challenging governance dimension: corruption in public procurement. In hopes of inspiring future research, the chapter critically reviews objective corruption proxies using administrative data on government purchases falling in four broad categories: tendering risk indicators, political connections indicators, supplier risk indicators, and contracting body risk indicators. The findings indicate that the best measurement instruments focus on the transaction level (micro level) while allowing for consistent aggregations for time series and cross-country comparisons. Such actionable indicators capture behaviour as directly as possible rather than remaining at the country level. They also retain the relational or transactional aspects of governance, revealing a much more dynamic picture than widely used population and expert surveys.

Capitoli di libri sul tema "Time series aggregation":

Stoker, Thomas M. "Aggregation (econometrics)". In Macroeconometrics and Time Series Analysis, 1–14. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280830_1.

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Drost, Feike C. "Temporal Aggregation of Time-Series". In Econometric Analysis of Financial Markets, 11–21. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-48666-1_2.

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Yager, Ronald R. "Intelligent Aggregation and Time Series Smoothing". In Time Series Analysis, Modeling and Applications, 53–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33439-9_3.

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Houthakker, H. S. "Proposed Technique for Estimating Demand Functions from Time Series". In Aggregation, Consumption and Trade, 255–56. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-1795-1_16.

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Akbarinia, Reza, e Florent Masseglia. "Aggregation-Aware Compression of Probabilistic Streaming Time Series". In Machine Learning and Data Mining in Pattern Recognition, 232–47. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21024-7_16.

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Valovich, Filipp. "Aggregation of Time-Series Data Under Differential Privacy". In Progress in Cryptology – LATINCRYPT 2017, 249–70. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25283-0_14.

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Castillo, Oscar, e Patricia Melin. "Type-3 Fuzzy Aggregation of Neural Networks". In Type-3 Fuzzy Logic in Time Series Prediction, 49–59. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59714-5_5.

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Durante, Fabrizio. "Copulas, Tail Dependence and Applications to the Analysis of Financial Time Series". In Aggregation Functions in Theory and in Practise, 17–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39165-1_3.

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Leontiadis, Iraklis, Kaoutar Elkhiyaoui e Refik Molva. "Private and Dynamic Time-Series Data Aggregation with Trust Relaxation". In Cryptology and Network Security, 305–20. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12280-9_20.

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Schurtenberger, Peter, Hugo Bissig, Luis Rojas, Ronny Vavrin, Anna Stradner, Sara Romer, Frank Scheffold e Veronique Trappe. "Aggregation and Gelation in Colloidal Suspensions: Time-Resolved Light and Neutron Scattering Experiments". In ACS Symposium Series, 143–60. Washington, DC: American Chemical Society, 2003. http://dx.doi.org/10.1021/bk-2003-0861.ch009.

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Atti di convegni sul tema "Time series aggregation":

Iosevich, S., G. Arutyunyants e Z. Hou. "Dynamic aggregation for time series forecasting". In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363996.

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de Oliveira, Ricardo T. A., Thaize Fernandes O. de Assis, Paulo Renato A. Firmino, Tiago A. E. Ferreira e Adriano L. I. Oliveira. "Aggregation of Time Series Forecasts via Cacoullos Copula". In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489098.

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Zhou, Ming, e Ye Chen. "An aggregation framework for time series-based MCDA". In 2011 International Conference on Grey Systems and Intelligent Services (GSIS 2011). IEEE, 2011. http://dx.doi.org/10.1109/gsis.2011.6044019.

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Bakondi, Bence Gabor, Andreas Peter, Maarten Everts, Pieter Hartel e Willem Jonker. "Publicly Verifiable Private Aggregation of Time-Series Data". In 2015 10th International Conference on Availability, Reliability and Security (ARES). IEEE, 2015. http://dx.doi.org/10.1109/ares.2015.82.

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Gujulla Leel, Srini Rohan, Vikrant Dey, Puspita Majumdar e Ankit Khairkar. "HierTGAN: Hierarchical Time Series Generation with Aggregation Constraints". In CODS-COMAD 2024: 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD). New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3632410.3632444.

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Yang, Peilin, Srikanth Thiagarajan e Jimmy Lin. "Robust, Scalable, Real-Time Event Time Series Aggregation at Twitter". In SIGMOD/PODS '18: International Conference on Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3183713.3190663.

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Werner, Gordon, Ahmet Okutan, Shanchieh Yang e Katie McConky. "Forecasting Cyberattacks as Time Series with Different Aggregation Granularity". In 2018 IEEE International Symposium on Technologies for Homeland Security (HST). IEEE, 2018. http://dx.doi.org/10.1109/ths.2018.8574185.

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Albers, Danielle, Michael Correll e Michael Gleicher. "Task-driven evaluation of aggregation in time series visualization". In CHI '14: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2556288.2557200.

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Saadallah, Amal, Maryam Tavakol e Katharina Morik. "An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting". In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. http://dx.doi.org/10.1109/icde51399.2021.00233.

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Kramakum, Chutimol, Thanawin Rakthanmanon e Kitsana Waiyamai. "Information gain Aggregation-based Approach for Time Series Shapelets Discovery". In 2018 10th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2018. http://dx.doi.org/10.1109/kse.2018.8573365.

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Rapporti di organizzazioni sul tema "Time series aggregation":

Jensen, Hans Grinsted, e Kym Anderson. Alternative Agricultural Price Distortions for CGE Analysis, 2007 and 2011. GTAP Research Memoranda, aprile 2014. http://dx.doi.org/10.21642/gtap.rm27.

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A recent World Bank research project has generated an annual time series of distortions to agricultural incentives over the past half century for 82 countries, the majority of which are low-and middle-income countries. In this memorandum, the current GTAP version 8 Data Base may be modified to incorporate this dataset, using an Altertax simulation. The files required for this Altertax simulation, including parameter files and shock files are generated by the DAItoGTAP.tab file which will accommodate any level of aggregation of the GTAP database. In this memorandum the data files required to modify the GTAP v8.1 database can be downloaded. Data required to modify the GTAP v9 database will be available at a later date when v9 is released. Keywords: Agricultural policies, World Bank DAI database, Extensions of the GTAP database, Developing countries

Hertel, Thomas, David Hummels, Maros Ivanic e Roman Keeney. How Confident Can We Be in CGE-Based Assessments of Free Trade Agreements? GTAP Working Paper, giugno 2003. http://dx.doi.org/10.21642/gtap.wp26.

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With the proliferation of Free Trade Agreements (FTAs) over the past decade, demand for quantitative analysis of their likely impacts has surged. The main quantitative tool for performing such analysis is Computable General Equilibrium (CGE) modeling. Yet these models have been widely criticized for performing poorly (Kehoe, 2002) and having weak econometric foundations (McKitrick, 1998; Jorgenson, 1984). FTA results have been shown to be particularly sensitive to the trade elasticities, with small trade elasticities generating large terms of trade effects and relatively modest efficiency gains, whereas large trade elasticities lead to the opposite result. Critics are understandably wary of results being determined largely by the authors’ choice of trade elasticities. Where do these trade elasticities come from? CGE modelers typically draw these elasticities from econometric work that uses time series price variation to identify an elasticity of substitution between domestic goods and composite imports (Alaouze, 1977; Alaouze, et al., 1977; Stern et al., 1976; Gallaway, McDaniel and Rivera, 2003). This approach has three problems: the use of point estimates as “truth”, the magnitude of the point estimates, and estimating the relevant elasticity. First, modelers take point estimates drawn from the econometric literature, while ignoring the precision of these estimates. As we will make clear below, the confidence one has in various CGE conclusions depends critically on the size of the confidence interval around parameter estimates. Standard “robustness checks” such as systematically raising or lowering the substitution parameters does not properly address this problem because it ignores information about which parameters we know with some precision and which we do not. A second problem with most existing studies derives from the use of import price series to identify home vs. foreign substitution, for example, tends to systematically understate the true elasticity. This is because these estimates take price variation as exogenous when estimating the import demand functions, and ignore quality variation. When quality is high, import demand and prices will be jointly high. This biases estimated elasticities toward zero. A related point is that the fixed-weight import price series used by most authors are theoretically inappropriate for estimating the elasticities of interest. CGE modelers generally examine a nested utility structure, with domestic production substitution for a CES composite import bundle. The appropriate price series is then the corresponding CES price index among foreign varieties. Constructing such an index requires knowledge of the elasticity of substitution among foreign varieties (see below). By using a fixed-weight import price series, previous estimates place too much weight on high foreign prices, and too small a weight on low foreign prices. In other words, they overstate the degree of price variation that exists, relative to a CES price index. Reconciling small trade volume movements with large import price series movements requires a small elasticity of substitution. This problem, and that of unmeasured quality variation, helps explain why typical estimated elasticities are very small. The third problem with the existing literature is that estimates taken from other researchers’ studies typically employ different levels of aggregation, and exploit different sources of price variation, from what policy modelers have in mind. Employment of elasticities in experiments ill-matched to their original estimation can be problematic. For example, estimates may be calculated at a higher or lower level of aggregation than the level of analysis than the modeler wants to examine. Estimating substitutability across sources for paddy rice gives one a quite different answer than estimates that look at agriculture as a whole. When analyzing Free Trade Agreements, the principle policy experiment is a change in relative prices among foreign suppliers caused by lowering tariffs within the FTA. Understanding the substitution this will induce across those suppliers is critical to gauging the FTA’s real effects. Using home v. foreign elasticities rather than elasticities of substitution among imports supplied from different countries may be quite misleading. Moreover, these “sourcing” elasticities are critical for constructing composite import price series to appropriate estimate home v. foreign substitutability. In summary, the history of estimating the substitution elasticities governing trade flows in CGE models has been checkered at best. Clearly there is a need for improved econometric estimation of these trade elasticities that is well-integrated into the CGE modeling framework. This paper provides such estimation and integration, and has several significant merits. First, we choose our experiment carefully. Our CGE analysis focuses on the prospective Free Trade Agreement of the Americas (FTAA) currently under negotiation. This is one of the most important FTAs currently “in play” in international negotiations. It also fits nicely with the source data used to estimate the trade elasticities, which is largely based on imports into North and South America. Our assessment is done in a perfectly competitive, comparative static setting in order to emphasize the role of the trade elasticities in determining the conventional gains/losses from such an FTA. This type of model is still widely used by government agencies for the evaluation of such agreements. Extensions to incorporate imperfect competition are straightforward, but involve the introduction of additional parameters (markups, extent of unexploited scale economies) as well as structural assumptions (entry/no-entry, nature of inter-firm rivalry) that introduce further uncertainty. Since our focus is on the effects of a PTA we estimate elasticities of substitution across multiple foreign supply sources. We do not use cross-exporter variation in prices or tariffs alone. Exporter price series exhibit a high degree of multicolinearity, and in any case, would be subject to unmeasured quality variation as described previously. Similarly, tariff variation by itself is typically unhelpful because by their very nature, Most Favored Nation (MFN) tariffs are non-discriminatory in nature, affecting all suppliers in the same way. Tariff preferences, where they exist, are often difficult to measure – sometimes being confounded by quantitative barriers, restrictive rules of origin, and other restrictions. Instead we employ a unique methodology and data set drawing on not only tariffs, but also bilateral transportation costs for goods traded internationally (Hummels, 1999). Transportation costs vary much more widely than do tariffs, allowing much more precise estimation of the trade elasticities that are central to CGE analysis of FTAs. We have highly disaggregated commodity trade flow data, and are therefore able to provide estimates that precisely match the commodity aggregation scheme employed in the subsequent CGE model. We follow the GTAP Version 5.0 aggregation scheme which includes 42 merchandise trade commodities covering food products, natural resources and manufactured goods. With the exception of two primary commodities that are not traded, we are able to estimate trade elasticities for all merchandise commodities that are significantly different form zero at the 95% confidence level. Rather than producing point estimates of the resulting welfare, export and employment effects, we report confidence intervals instead. These are based on repeated solution of the model, drawing from a distribution of trade elasticity estimates constructed based on the econometrically estimated standard errors. There is now a long history of CGE studies based on SSA: Systematic Sensitivity Analysis (Harrison and Vinod, 1992; Wigle, 1991; Pagon and Shannon, 1987) Ho

Martínez-Rivera, Wilmer, Eliana R. González-Molano e Edgar Caicedo-García. Forecasting Inflation from Disaggregated Data: The Colombian case. Banco de la República, ottobre 2023. http://dx.doi.org/10.32468/be.1251.

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Based on monthly disaggregated Consumer Price Index (CPI) item series and macroeconomic series, we explore the advantages of forecast inflation from a disaggregated to an aggregated level by aggregating the forecasts. We compare the performance of this approach with the forecast obtained modeling aggregated inflation directly. For the aggregate level, we implement some of the techniques and models, helpful to work with many predictors, such as dimension reduction, shrinkage methods, and machine learning models. Also, we implement traditional time-series models. For the disaggregated data, we use its lags and a set of macroeconomic variables as explanatory variables. Direct and recursive forecast techniques are also explored. The sample period of the analysis is from 2011 to 2022, with forecasting and evaluation out of the sample from 2017. In addition, we evaluate the forecast accuracy during the COVID-19 period. We found a reduction in the forecast error from the disaggregate analysis over the aggregate one.

Tsur, Yacov, David Zilberman, Uri Shani, Amos Zemel e David Sunding. Dynamic intraseasonal irrigation management under water scarcity, water quality, irrigation technology and environmental constraints. United States Department of Agriculture, marzo 2007. http://dx.doi.org/10.32747/2007.7696507.bard.

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In this project we studied optimal use and adoption of sophisticated irrigation technologies. The stated objectives in the original proposal were to develop a conceptual framework for analyzing intra-season timing of water application rates with implications for crop and irrigation technology selection. We proposed to base the analysis on an intra-seasonal, dynamic, agro-economic model of plants' water demand, paying special attention to contamination of groundwater and soil in intensively cultivated areas that increasingly rely on water of lesser quality. The framework developed in the project integrates (i) a bio-physical model of water flow in the vadose zone and water uptake by plants and yield response with (ii) a dynamic management model to determine the optimal intra-season irrigation policy. It consists of a dynamic optimization model to determine irrigation rates at each point of time during the growing season and aggregation relating harvested yield with accumulated water input. The detailed dynamic approach provides a description of yield production processes at the plant’s level, and serves to determine intra-season irrigation decisions. Data derived from extensive field experiments were used to calibrate the model's parameters. We use the framework to establish the substitution between irrigation technology (capital) and water inputs; this is an important property of irrigation water productivity that has been overlooked in the literature. Another important feature investigated is the possibility to substitute fresh and saline water with a minimal productivity loss. The effects of soil properties and crop characteristics on optimal technology adoption have also been studied. We find that sandy soil, with low water holding capacity, is more conducive to adoption of sophisticated drip irrigation, as compared to heavier soils in which drainage losses are significantly smaller.

Chefetz, Benny, Baoshan Xing, Leor Eshed-Williams, Tamara Polubesova e Jason Unrine. DOM affected behavior of manufactured nanoparticles in soil-plant system. United States Department of Agriculture, gennaio 2016. http://dx.doi.org/10.32747/2016.7604286.bard.

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The overall goal of this project was to elucidate the role of dissolved organic matter (DOM) in soil retention, bioavailability and plant uptake of silver and cerium oxide NPs. The environmental risks of manufactured nanoparticles (NPs) are attracting increasing attention from both industrial and scientific communities. These NPs have shown to be taken-up, translocated and bio- accumulated in plant edible parts. However, very little is known about the behavior of NPs in soil-plant system as affected by dissolved organic matter (DOM). Thus DOM effect on NPs behavior is critical to assessing the environmental fate and risks related to NP exposure. Carbon-based nanomaterials embedded with metal NPs demonstrate a great potential to serve as catalyst and disinfectors. Hence, synthesis of novel carbon-based nanocomposites and testing them in the environmentally relevant conditions (particularly in the DOM presence) is important for their implementation in water purification. Sorption of DOM on Ag-Ag₂S NPs, CeO₂ NPs and synthesized Ag-Fe₃O₄-carbon nanotubebifunctional composite has been studied. High DOM concentration (50mg/L) decreased the adsorptive and catalytic efficiencies of all synthesized NPs. Recyclable Ag-Fe₃O₄-carbon nanotube composite exhibited excellent catalytic and anti-bacterial action, providing complete reduction of common pollutants and inactivating gram-negative and gram-positive bacteria at environmentally relevant DOM concentrations (5-10 mg/L). Our composite material may be suitable for water purification ranging from natural to the industrial waste effluents. We also examined the role of maize (Zeamays L.)-derived root exudates (a form of DOM) and their components on the aggregation and dissolution of CuONPs in the rhizosphere. Root exudates (RE) significantly inhibited the aggregation of CuONPs regardless of ionic strength and electrolyte type. With RE, the critical coagulation concentration of CuONPs in NaCl shifted from 30 to 125 mM and the value in CaCl₂ shifted from 4 to 20 mM. This inhibition was correlated with molecular weight (MW) of RE fractions. Higher MW fraction (> 10 kDa) reduced the aggregation most. RE also significantly promoted the dissolution of CuONPs and lower MW fraction (< 3 kDa) RE mainly contributed to this process. Also, Cu accumulation in plant root tissues was significantly enhanced by RE. This study provides useful insights into the interactions between RE and CuONPs, which is of significance for the safe use of CuONPs-based antimicrobial products in agricultural production. Wheat root exudates (RE) had high reducing ability to convert Ag+ to nAg under light exposure. Photo-induced reduction of Ag+ to nAg in pristine RE was mainly attributed to the 0-3 kDa fraction. Quantification of the silver species change over time suggested that Cl⁻ played an important role in photoconversion of Ag+ to nAg through the formation and redox cycling of photoreactiveAgCl. Potential electron donors for the photoreduction of Ag+ were identified to be reducing sugars and organic acids of low MW. Meanwhile, the stabilization of the formed particles was controlled by both low (0-3 kDa) and high (>3 kDa) MW molecules. This work provides new information for the formation mechanism of metal nanoparticles mediated by RE, which may further our understanding of the biogeochemical cycling and toxicity of heavy metal ions in agricultural and environmental systems. Copper sulfide nanoparticles (CuSNPs) at 1:1 and 1:4 ratios of Cu and S were synthesized, and their respective antifungal efficacy was evaluated against the pathogenic activity of Gibberellafujikuroi(Bakanae disease) in rice (Oryza sativa). In a 2-d in vitro study, CuS decreased G. fujikuroiColony- Forming Units (CFU) compared to controls. In a greenhouse study, treating with CuSNPs at 50 mg/L at the seed stage significantly decreased disease incidence on rice while the commercial Cu-based pesticide Kocide 3000 had no impact on disease. Foliar-applied CuONPs and CuS (1:1) NPs decreased disease incidence by 30.0 and 32.5%, respectively, which outperformed CuS (1:4) NPs (15%) and Kocide 3000 (12.5%). CuS (1:4) NPs also modulated the shoot salicylic acid (SA) and Jasmonic acid (JA) production to enhance the plant defense mechanisms against G. fujikuroiinfection. These results are useful for improving the delivery efficiency of agrichemicals via nano-enabled strategies while minimizing their environmental impact, and advance our understanding of the defense mechanisms triggered by the NPs presence in plants.

Eparkhina, Dina. EuroSea Legacy Report. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d8.12.

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EuroSea is a holistic large-scale project encompassing the full value chain of marine knowledge, from observations to modelling and forecasting and to user-focused services. This report summarizes the legacy of EuroSea as planned and measured through a dedicated impact monitoring protocol, a holistic assessment of the project's successes in advancing and integrating European ocean observing and forecasting systems. Since its start, EuroSea has been analysing how well the project progresses towards the identified areas of impact. Impact assessment is not performance evaluation. These terms overlap but are distinct: performance relates to the efficient use of resources; impact relates to the transformative effect on the users. The EuroSea legacy report is presented through an aggregation and analysis of the EuroSea work towards achieving its impacts. Overall, over 100 impacts have been identified and presented on the website and in a stand-alone impact report. The legacy report sheds light on 32 most powerful impacts (four impacts in each of the eight EuroSea impact areas). EuroSea Impact Areas: 1. Strengthen the European Ocean Observing System (EOOS), support the Global Ocean Observing System (GOOS) and the GOOS Regional Alliances; 2. Increase ocean data sharing and integration; 3. Deliver improved climate change predictions; 4. Build capacity, internally in EuroSea and externally with EuroSea users, in a range of key areas; 5. Develop innovations, including exploitation of novel ideas or concepts; shorten the time span between research and innovation and foster economic value in the blue economy; 6. Facilitate methodologies, best practices, and knowledge transfer in ocean observing and forecasting; 7. Contribute to policy making in research, innovation, and technology; 8. Raise awareness of the need for a fit for purpose, sustained, observing and forecasting system in Europe. Ocean observing and forecasting is a complex activity brining about a variety of technologies, human expertise, in water and remote sensing measurements, high-volume computing and artificial intelligence, and a high degree of governance and coordination. Determining an impact on a user type or an area, therefore, requires a holistic assessment and a clear strategic overview. The EuroSea impact monitoring protocol has been the first known such attempt in a European ocean observing and forecasting project. The project’s progress has been followed according to the identified impact areas, through consortium workshops, stakeholder webinars, tracking, and reporting. At the end of EuroSea, we are able to demonstrate how well we have responded to the European policy drivers set out in the funding call and the grant agreement of our project, signed between the European Commission and 53 organizations, members of the EuroSea consortium. The project's impact is diverse, spanning areas from strengthening ocean observing governance to contributing to policymaking or boosting ocean research, innovation, and technology. Each impact area underscores EuroSea's commitment to a sustainable and informed approach to ocean observing and forecasting for enhanced marine knowledge and science-based sustainable blue economy and policies. (EuroSea Deliverable, D8.12)

Opportunities, challenges and evidence needs for investing in smallholder farming. Commercial Agriculture for Smallholders and Agribusiness (CASA), 2019. http://dx.doi.org/10.1079/20240191176.

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Commercial Agriculture for Smallholders and Agribusiness (CASA) is a flagship programme financed by the UK Department for International Development (DFID), which seeks to increase economic opportunities for smallholders to step and trade into growing commercial markets. The programme aims to increase investments in agribusinesses which source from smallholder farmers and to generate new evidence and research that amplifies the case for doing business with smallholders. During the inception phase, CASA conducted a survey of 25 investors and investing support stakeholders, to determine key constraints, opportunities and evidence needs for unlocking investment opportunities in agribusinesses and smallholder farmers globally, and to identify information needs and seeking behaviours. The three top constraints identified were: limited availability of investable agribusinesses; low productivity of smallholder farmers; and existing gaps in infrastructure and value chains. The main opportunities for increased investment are: investing and improving infrastructure; incubating and supporting early stage businesses with donor funding and technical assistance; and supporting shareholder services like aggregation and mechanisation initiatives. The main evidence gaps identified through the survey are: case studies and examples of profitable and impactful business models; a mapping of actors involved in agricultural finance; and crop and country specific data on productivity and impact. Despite the widespread agreement on the key issues, the survey also identified divergent opinions: inappropriately applied grants and donor subsidies create uneven competition for commercial investors and can harm value chains, while less commercial investors emphasised the importance of these subsidies for derisking their investments. There was agreement among investors that technical assistance needs to be used appropriately and not to finance operations, while this observation was not mentioned by investment support stakeholders. Additionally, having a minimum ticket size was mentioned by investors as a critical factor, while this was not such a priority for investment support stakeholders. One of the main priorities emerged from this survey, and with the aim of achieving additionality of existing investments, would be to produce a map of investors, types of investment and support service in specific countries, as well as a list of investable agribusinesses. This resource could potentially be co-financed and shared by investors targeting smallholder agribusinesses, which would help them reduce the time and cost needed to identify direct and indirect investment opportunities. New partnerships with on-the-ground networks combined with technical assistance, incubators, and grant funding would also help reduce some of the current risks related to investing in smallholder farmers and agribusiness.