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Aplicação de redes neurais artificiais na análise de séries temporais econômico-financeiras; Artificial neural networks application in financial-economic time series analysis

Oliveira, Mauri Aparecido de
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 07/12/2007 PT
Relevância na Pesquisa
85.78%
Diversas metodologias são empregadas para realizar a análise de séries temporais, dentre as quais destaca-se o uso das redes neurais artificiais (RNA). Neste trabalho são utilizados quatro métodos para realizar previsão de séries temporais univariadas: os modelos ARIMAGARCH, RNA feedforward, RNA treinada com filtro de Kalman estendido (EKF) e RNA treinada com o filtro de Kalman unscented (UKF). Sendo que o uso de RNA-UKF é um avanço recente na área de sistemas de inteligência computacional. O uso de redes neurais treinadas com filtro de Kalman é uma metodologia que tem trazido bons resultados em uma ampla variedade de aplicações nas áreas comercial, militar e científica. Em 2002 aproximadamente 250 bilhões de dólares eram gerenciados em fundos de investimentos por modelos quantitativos (tais como lógica fuzzy, redes neurais, algoritmos genéticos, fractais e modelos de Markov). Desde 2006 estima-se que três em cada dez destes fundos utilizem estes modelos quantitativos. A capacidade das RNA em lidar com não linearidades é uma vantagem normalmente destacada quando são realizadas previsões de séries temporais. São apresentadas simulações de Monte Carlo que mostram a influência dos parâmetros dos modelos ARIMA-GARCH na predição de redes neurais artificiais do tipo feedforward...

Algoritmo kNN para previsão de dados temporais: funções de previsão e critérios de seleção de vizinhos próximos aplicados a variáveis ambientais em limnologia; Time series prediction using a KNN-based algorithm prediction functions and nearest neighbor selection criteria applied to limnological data

Ferrero, Carlos Andres
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 04/03/2009 PT
Relevância na Pesquisa
75.76%
A análise de dados contendo informações sequenciais é um problema de crescente interesse devido à grande quantidade de informação que é gerada, entre outros, em processos de monitoramento. As séries temporais são um dos tipos mais comuns de dados sequenciais e consistem em observações ao longo do tempo. O algoritmo k-Nearest Neighbor - Time Series Prediction kNN-TSP é um método de previsão de dados temporais. A principal vantagem do algoritmo é a sua simplicidade, e a sua aplicabilidade na análise de séries temporais não-lineares e na previsão de comportamentos sazonais. Entretanto, ainda que ele frequentemente encontre as melhores previsões para séries temporais parcialmente periódicas, várias questões relacionadas com a determinação de seus parâmetros continuam em aberto. Este trabalho, foca-se em dois desses parâmetros, relacionados com a seleção de vizinhos mais próximos e a função de previsão. Para isso, é proposta uma abordagem simples para selecionar vizinhos mais próximos que considera a similaridade e a distância temporal de modo a selecionar os padrões mais similares e mais recentes. Também é proposta uma função de previsão que tem a propriedade de manter bom desempenho na presença de padrões em níveis diferentes da série temporal. Esses parâmetros foram avaliados empiricamente utilizando várias séries temporais...

Improving time series modeling by decomposing and analysing stochastic and deterministic influences; Modelagem de séries temporais por meio da decomposição e análise de influências estocásticas e determinísticas

Rios, Ricardo Araújo
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 22/10/2013 EN
Relevância na Pesquisa
85.82%
This thesis presents a study on time series analysis, which was conducted based on the following hypothesis: time series influenced by additive noise can be decomposed into stochastic and deterministic components in which individual models permit obtaining a hybrid one that improves accuracy. This hypothesis was confirmed in two steps. In the first one, we developed a formal analysis using the Nyquist-Shannon sampling theorem, proving Intrinsic Mode Functions (IMFs) extracted from the Empirical Mode Decomposition (EMD) method can be combined, according to their frequency intensities, to form stochastic and deterministic components. Considering this proof, we designed two approaches to decompose time series, which were evaluated in synthetic and real-world scenarios. Experimental results confirmed the importance of decomposing time series and individually modeling the deterministic and stochastic components, proving the second part of our hypothesis. Furthermore, we noticed the individual analysis of both components plays an important role in detecting patterns and extracting implicit information from time series. In addition to these approaches, this thesis also presents two new measurements. The first one is used to evaluate the accuracy of time series modeling in forecasting observations. This measurement was motivated by the fact that existing measurements only consider the perfect match between expected and predicted values. This new measurement overcomes this issue by also analyzing the global time series behavior. The second measurement presented important results to assess the influence of the deterministic and stochastic components on time series observations...

Utilização de series temporais de imagens AVHRR/NOAA no apoio a estimativa operacional da produção da cana-de-açucar no Estado de São Paulo; Use of time series of AVHRR/NOAA in support of operational estimates of production of cane sugar in the State of São Paulo

Cristina Rodrigues Nascimento
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 11/02/2010 PT
Relevância na Pesquisa
75.76%
O Brasil é líder mundial na fabricação, exportação de açúcar e na produção de álcool. O estado de São Paulo responde por 60% da produção de açúcar e 61% de todo o álcool produzido no país. Em função da alta relevância da produção, é importante que se tenham estimativas e levantamentos seguros das áreas cultivadas com a cultura. O avanço das diferentes técnicas de sensoriamento remoto tem permitido utilizar imagens de satélites para monitorar e auxiliar a estimativa dessas áreas. São inúmeras as opções, entre elas as imagens do sensor AVHRR/NOAA. Aliando a necessidade de obter estimativas mais precisas das safras de cana-de-açúcar, com o potencial de adquirir informações agrícolas da cultura através do NDVI, o presente trabalho explorou a análise de séries temporais das imagens NDVI/AVHRR, na identificação das áreas com cana-de-açúcar no Estado de São Paulo. A partir da identificação operacional, foram selecionados municípios com áreas expressivas a fim de testar a viabilidade do uso de um modelo fenológico-espectral, no fornecimento de informações objetivas que possam auxiliar os sistemas de previsão de safras. Os resultados apontam que as áreas com cana-de-açúcar foram bem modeladas...

Modelos lineares generalizadas para series temporais com memoria longa; Generalized linear models for long memory time series

Cristiano Amancio Vieira Borges
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 29/01/2010 PT
Relevância na Pesquisa
75.78%
A modelagem de séries temporais não gaussianas é um tema de alta relevância na análise de séries temporais. Utilizando-se de estimação por verossimilhança parcial, Kedem e Fokianos (2002) estenderam sistematicamente a metodologia dos Modelos Lineares Generalizados (MLG) para séries temporais em que tanto a série de interesse quanto as covariáveis são estocasticamente dependentes. Entretanto, a análise estatística de séries com memória longa (ML), seja na resposta ou nas covariáveis, não é discutida em detalhes. O primeiro objetivo desta dissertação é investigar, através de simulações, as propriedades dos estimadores de máxima verossimilhança parcial dos coeficientes do MLG quando utilizado para séries temporais com ML. O segundo objetivo consiste em um estudo sobre a qualidade das previsões obtidas para vários modelos ajustados a dados de séries com ML, utilizando a metodologia proposta por Kedem e Fokianos (2002). Os modelos considerados nesta dissertação são modelos para séries de contagens, séries binárias e séries categóricas ordinais. Finalmente, as metodologias são ilustradas através de aplicações em conjuntos de dados reais de finanças e de poluição do ar; Non-gaussian time series modeling is a high relevance issue of time series analysis. Kedem and Fokianos (2002) have used partial likelihood estimation to extend the Generalized Linear Models (GLM) methodology systematically to time series where the response and covariate data are both stochastically dependent. However...

Serial Annotator : managing annotations of time series = Serial Annotator: gerenciando anotações em séries temporais; Serial Annotator : gerenciando anotações em séries temporais

Felipe Henriques da Silva
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 10/06/2013 PT
Relevância na Pesquisa
75.78%
Séries temporais são sequências de valores medidos em sucessivos instantes de tempo. Elas são usadas em diversos domínios, tais como agricultura, medicina e economia. A análise dessas séries é de extrema importância, fornecendo a especialistas a capacidade de identificar tendências e prever possíveis cenários. A fim de facilitar sua análise, especialistas frequentemente associam anotações com séries temporais. Tais anotações também podem ser usadas para correlacionar séries distintas, ou para procurar por séries específicas num banco de dados. Existem muitos desafios envolvidos no gerenciamento destas anotações - desde encontrar estruturas adequadas para associá-las com as séries, até organizar e recuperar séries através das anotações associadas a estas. Este trabalho contribui para o trabalho em gerenciamento de séries temporais. Suas principais contribuições são o projeto e desenvolvimento de um arcabouço para o gerenciamento de múltiplas anotações associadas com uma ou mais séries em um banco de dados. Este arcabouço também fornece meios para o controle de versão das anotações, de modo que os estados anteriores de uma anotação nunca sejam perdidos. Serial Annotator é uma aplicação desenvolvida para a plataforma Android. Ela foi usada para validar o arcabouço proposto e foi testada com dados reais envolvendo problemas do domínio agrícola.; Time series are sequences of values measured at successive time instants. They are used in several domains such as agriculture...

Subspace techniques and biomedical time series analysis

Tomé, A. M.; Teixeira, A. R.; Lang, E. W.
Fonte: Bentham Science Publishers Publicador: Bentham Science Publishers
Tipo: Parte de Livro
ENG
Relevância na Pesquisa
85.7%
The application of subspace techniques to univariate (single-sensor) biomedical time series is presented. Both linear and non-linear methods are described using algebraic models, and the dot product is the most important operation concerning data manipulations. The covariance/correlationmatrices, computed in the space of time-delayed coordinates or in a feature space created by a non-linear mapping, are employed to deduce orthogonal models. Linear methods encompass singular spectrum analysis (SSA), singular value decomposition (SVD) or principal component analysis (PCA). Local SSA is a variant of SSA which can approximate non-linear trajectories of the embedded signal by introducing a clustering step. Generically non-linear methods encompass kernel principal component analysis (KPCA) and greedy KPCA. The latter is a variant where the subspace model is based on a selected subset of data only.; FCT - SFRH/BD/28404/2006

Time series analysis of water surface temperature and heat flux components in the Itumbiara Reservoir (GO), Brazil

Alcântara,Enner Herenio de; Stech,José Luiz; Lorenzzetti,João Antônio; Novo,Evlyn Márcia Leão de Moraes
Fonte: Associação Brasileira de Limnologia Publicador: Associação Brasileira de Limnologia
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/09/2011 EN
Relevância na Pesquisa
75.78%
AIM: Water temperature plays an important role in ecological functioning and in controlling the biogeochemical processes of the aquatic system. Conventional water quality monitoring is expensive and time consuming. It is particularly challenging for large water bodies. Conversely, remote sensing can be considered a powerful tool to assess important properties of aquatic systems because it provides synoptic and frequent data acquisition over large areas. The objective of this study was to analyze time series of surface water temperature and heat flux to advance the understanding of temporal variations in a hydroelectric reservoir. METHOD: MODIS water-surface temperature (WST) level 2, 1 km nominal resolution data (MOD11L2, version 5) were used. All available clear-sky MODIS/Terra images from 2003 to 2008 were used, resulting in a total of 786 daytime and 473 nighttime images. Time series of surface water temperature was obtained computing the monthly mean in a 3×3 window of three reservoir selected sites: 1) near the dam, 2) at the centre of the reservoir and 3) in the confluence of the rivers. In-situ meteorological data from 2003 to 2008 were used to calculate surface energy budget time series. Cross-wavelet, coherence and phase analysis were carried out to compute the correlation between daytime and nighttime surface water temperatures and the computed heat fluxes. RESULTS: The monthly mean of the day-time WST shows lager variability than the night-time WST. All time series (daytime and nighttime) have a cyclical pattern...

Integrating time series mining and fractals to discover patterns and extreme events in climate and remote sensing databases.

ROMANI, L. A. S.
Fonte: 2010. Publicador: 2010.
Tipo: Teses/dissertações (ALICE) Formato: 179 p.
EN
Relevância na Pesquisa
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This thesis presents new methods based on fractal theory and data mining techniques to support agricultural monitoring in regional scale, specifically regions with sugar cane fields. This commodity greatly contributes to the Brazilian economy since it is a viable alternative to replace fossil fuels. Since climate influences the national agricultural production, researchers use climate data associated to agrometeorological indexes, and recently they also employed data from satellites to support decision making processes. In this context, we proposed a method that uses the fractal dimension to identify trend changes in climate series jointly with a statistical analysis module to define which attributes are responsible for the behavior alteration in the series. Moreover, we also proposed two methods of similarity measure to allow comparisons among different agricultural regions represented by multiples variables from meteorological data and remote sensing images. Given the importance of studying the extreme weather events, which could increase in intensity, duration and frequency according to different scenarios indicated by climate forecasting models, we proposed the CLIPSMiner algorithm to identify relevant patterns and extremes in climate series. CLIPSMiner also detects correlations among multiple time series considering time lag and finds patterns according to parameters...

Self projecting time series forecast: an online stock trend forecast system

Deng, K.; Shen, H.; Tian, H.
Fonte: Inderscience Publishers Publicador: Inderscience Publishers
Tipo: Artigo de Revista Científica
Publicado em //2006 EN
Relevância na Pesquisa
85.71%
This paper explores the applicability of time series analysis for stock trend forecast and presents the Self projecting Time Series Forecasting (STSF) System which we have developed. The basic idea behind this system is the online discovery of mathematical formulae that can approximately generate historical patterns from given time series. SPTF offers a set of combined prediction functions for stocks, including Point Forecast and Confidence Interval Forecast, where the latter could be considered as a subsidiary index of the former in the process of decision making. We propose a new approach to determine the support line and resistance line that are essential for market assessment. Empirical tests have shown that the hit rate of the prediction is impressively high if the model is properly selected, indicating a good accuracy and efficiency of this approach. The numerical forecast result of STSF is superior to normal descriptive investment recommendation offered by most web brokers. Furthermore, SPTF is an online system and investors and analysts can upload their real time data to get the forecast result on the web.; Ke Deng, Hong Shen, Hui Tian

Forecasting high waters at Venice Lagoon using chaotic time series analisys and nonlinear neural netwoks

Zaldívar, J.M.; Gutiérrez, E.; Galván, Inés M.; Strozzi, F.; Tomasin, A.
Fonte: IWA Publishing Publicador: IWA Publishing
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2000 ENG
Relevância na Pesquisa
85.66%
Time series analysis using nonlinear dynamics systems theory and multilayer neural networks models have been applied to the time sequence of water level data recorded every hour at 'Punta della Salute' from Venice Lagoon during the years 1980-1994. The first method is based on the reconstruction of the state space attractor using time delay embedding vectors and on the characterisation of invariant properties which define its dynamics. The results suggest the existence of a low dimensional chaotic attractor with a Lyapunov dimension, DL, of around 6.6 and a predictability between 8 and 13 hours ahead. Furthermore, once the attractor has been reconstructed it is possible to make predictions by mapping local-neighbourhood to local-neighbourhood in the reconstructed phase space. To compare the prediction results with another nonlinear method, two nonlinear autoregressive models (NAR) based on multilayer feedforward neural networks have been developed. From the study, it can be observed that nonlinear forecasting produces adequate results for the 'normal' dynamic behaviour of the water level of Venice Lagoon, outperforming linear algorithms, however, both methods fail to forecast the 'high water' phenomenon more than 2-3 hours ahead.

A time series analysis of U.S. Army enlisted force loss rates

DeWald, Edward T.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado Formato: xiii, 92 p.
EN_US
Relevância na Pesquisa
95.7%
Approved for public release; distribution is unlimited; The analysis and prediction of personnel loss behavior is critical to effective manpower planning and to the U.S. Army's Enlisted Personnel Strength Management System (EPSMS). In support of efforts to modernize the EPSMS, this thesis examines the method by which the Enlisted Loss Inventory Model (ELIM) analyzes loss rates and forecasts them into the future. Time series analysis techniques seek to identify patterns in data and forecast them into the future via time based extrapolations. Four such methods were used to construct loss rate forecasts from data. These methods were the arithmetic mean, exponential smoothing (the current ELIM method), seasonal exponential smoothing and an autoregressive moving average model. Forecasted rates were used to project force strengths which were in fact known. The resulting errors in forecasted strength were analyzed, compared and contrasted with respect to the methods. Error analysis revealed no significant performance differences between the methods. Hence, the simplest methods (mean and exponential smoothing) may be viewed as more economical and preferred; http://archive.org/details/timeseriesanalys00dewa; Captain, United States Marine Corps

A time series analysis of U.S. Army officer loss rates; A time series analysis of United States Army officer loss rates

Sparling, Steven J.
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado Formato: xiv, 83 p. : col. ill. ;
Relevância na Pesquisa
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Accurate prediction of officer loss behavior is essential for the planning of personnel policies and executing the U.S. Army's Officer Personnel Management System (OPMS). Inaccurate predictions of officer strength affect the number of personnel authorizations, the Army's budget, and the necessary number of accessions. Imbalances of officer strength in the basic branches affect the Army's combat readiness as a whole. Captains and majors comprise a critical management population in the United States Army's officer corps. This thesis analyzes U.S. Army officer loss rates for captains and majors and evaluates the fit of several time series models. The results from this thesis validate the time series forecasting technique currently used by the Army G-1, Winters-method additive.

Air Pollution and Health: Time Series Tools and Analysis

Burr, WESLEY SAMUEL
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado
EN; EN
Relevância na Pesquisa
85.76%
This thesis is concerned, loosely, with time series analysis. It is also, loosely, concerned with smoothers and Generalized Additive Models. And, finally, it is also concerned with the estimation of health risk due to air pollution. In the field of time series analysis, we develop two data-driven interpolation algorithms for interpolation of mixed time series data; that is, data which has a stationary or “almost” stationary background with embedded deterministic trend and sinusoidal components. These interpolators are developed to deal with the problem of estimating power spectra under the condition that some observations of the series are unavailable. We examine the structure of time-based cubic regression spline smoothers in Generalized Additive Models and demonstrate several interpretation problems with the resultant models. We propose, implement, and test a replacement smoother and show dramatic improvement. We further demonstrate a new, spectrally motivated way of examining residuals in Generalized Additive Models which drives many of the findings of this thesis. Finally, we create and analyze a large-scale Canadian air pollution and mortality database. In the course of analyzing the data we rebuild the standard risk estimation model and demonstrate several improvements. We conclude with a comparison of the original model and the updated model and show that the new model gives consistently more positive risk estimates.; Thesis (Ph.D...

Detecção de danos estruturais usando analise de series temporais e atuadores e sensores piezeletricos; Structural damage detection using time series analysis and piezoelectries actuators and sensors

Samuel d Silva
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 14/02/2008 PT
Relevância na Pesquisa
95.74%
A contribuição deste trabalho foi desenvolver uma metodologia para detecção e localização de danos considerando apenas respostas de deslocamento ou aceleração e medidas obtidas por atuadores e sensores piezelétricos (PZTs) distribuídos e colados em estruturas flexíveis. Modelos de filtros discretos do tipo auto-regressivos, como AR-ARX, ARMA e ARMAX, são usados para extrair um indicador de danos a partir dos erros de predição linear destes filtros. Investiga-se também o uso de séries discretas de Wiener/Volterra escritas com filtros de Kautz para obtenção de erros de predição não-lineares. Para classificar os erros de predição (lineares ou não-lineares) nas classes 'sem dano' ou 'com dano' comparou-se o uso de ferramentas não-supervisionadas de classificação de padrões estatísticos, como agrupamento fuzzy e controle estatístico de processos. Testes numéricos e experimentais foram realizados e os resultados alcançados com a metodologia desenvolvida apresentaram vantagens em relação aos métodos convencionais que são discutidas no decorrer do trabalho; This work proposes a novel approach to detect and locate incipient damage in structures by using only acceleration responses and coupled piezoelectric actuators and sensors. Though the major focus in smart damage detection is given by on the monitoring of the electrical impedance in the frequency domain...

Estudio fluidodinámico de reactores multifásicos mediante técnicas de análisis no-invasivas; Study of multiphase systems using non invasive techniques of analysis

Fraguío, María Sol
Fonte: Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires Publicador: Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires
Tipo: info:eu-repo/semantics/doctoralThesis; tesis doctoral; info:eu-repo/semantics/publishedVersion Formato: application/pdf
Publicado em //2010 SPA
Relevância na Pesquisa
75.79%
Este trabajo presenta un estudio sobre algunos aspectos de la fluidodinámica de sistemas multifásicos utilizando técnicas que no perturban el movimiento de los fluidos y/o el sólido presentes. El objetivo es caracterizar y/o monitorear la fluidodinámica en sistemas de interés industrial, particularmente, columnas de burbujeo bifásicas y lechos fluidizados trifásicos. Para el estudio de los sistemas multifásicos involucrados en este trabajo se midieron y analizaron series temporales provenientes de experimentos de densitometría, de tomografía de emisión de partículas únicas, generalmente llamada ”Radioactive Particle Tracking” (RPT), y de fluctuaciones de presión. Se utilizaron técnicas estadísticas básicas y un test estadístico que permite tener en cuenta las características caóticas de los sistemas para determinar transiciones de régimen de flujo y/o monitorear un cambio brusco en la fluidodinámica del sistema. El análisis de las diversas series temporales registradas permitió proponer procedimientos de identificación de transiciones del régimen de flujo subyacente, habiéndose obtenido concordancia entre las predicciones que surgen del análisis de series temporales de distintas variables características. Asimismo...

Highly comparative time-series analysis: The empirical structure of time series and their methods

Fulcher, Ben D.; Little, Max A.; Jones, Nick S.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 03/04/2013
Relevância na Pesquisa
75.83%
The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording, and analyzing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series and over 9000 time-series analysis algorithms are analyzed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines, and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series...

Topics in Time Series Analysis of Water Quality Data

Di Toro, D. M.; O'Connor, D. J.
Fonte: Universidade de Delaware Publicador: Universidade de Delaware
Tipo: Relatório Formato: 3070082 bytes; application/pdf
EN_US
Relevância na Pesquisa
95.69%
The results of the time series analysis also indicate the effect the load variability can have on the water quality of a stream. Indeed, in the final analysis, this is the primary reason for being concerned with plant variations. Dissolved oxygen variations of 1.0 mg/l or greater appear to be possible from a mathematical model of the stream and the observed treatment plant spectrum. The theory indicates that the maximum DO variability will occur at the location of the critical DO deficit.

Prediction and nonparametric estimation for time series analysis with heavy tails

Hall, Peter; Peng, L; Yao, Qiwei
Fonte: Blackwell Publishing Ltd Publicador: Blackwell Publishing Ltd
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
85.68%
Motivated by prediction problems for time series with heavy-tailed marginal distributions, we consider methods based on 'local least absolute deviations' for estimating a regression median from dependent data. Unlike more conventional 'local median' methods, which are in effect based on locally fitting a polynomial of degree 0, techniques founded on local least absolute deviations have quadratic bias right up to the boundary of the design interval. Also in contrast to local least-squares methods based on linear fits, the order of magnitude of variance does not depend on tail-weight of the error distribution. To make these points clear, we develop theory describing local applications to time series of both least-squares and least-absolute-deviations methods, showing for example that, in the case of heavy-tailed data, the conventional local-linear least-squares estimator suffers from an additional bias term as well as increased variance.

Detecting a global warming signal in hemispheric temperature series: a structural time series analysis

Stern, David; Kaufmann, R K
Fonte: Kluwer Academic Publishers Publicador: Kluwer Academic Publishers
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
85.75%
Non-stationary time series such as global and hemispheric temperatures, greenhouse gas concentrations, solar irradiance, and anthropogenic sulfate aerosols, may contain stochastic trends (the simplest stochastic trend is a random walk) which, due to their unique patterns, can act as a signal of the influence of other variables on the series in question. Two or more series may share a common stochastic trend, which indicates that either one series causes the behavior of the other or that there is a common driving variable. Recent developments in econometrics allow analysts to detect and classify such trends and analyze relationships among series that contain stochastic trends. We apply some univariate autoregression based tests to evaluate the presence of stochastic trends in several time series for temperature and radiative forcing. The temperature and radiative forcing series are found to be of different orders of integration which would cast doubt on the anthropogenic global warming hypothesis. However, these tests can suffer from size distortions when applied to noisy series such as hemispheric temperatures. We, therefore, use multivariate structural time series techniques to decompose Northern and Southern Hemisphere temperatures into stochastic trends and autoregressive noise processes. These results show that there are two independent stochastic trends in the data. We investigate the possible origins of these trends using a regression method. Radiative forcing due to greenhouse gases and solar irradiance can largely explain the common trend. The second trend...