To see the other types of publications on this topic, follow the link: Vector autoregressive moving average.
Dissertations / Theses on the topic 'Vector autoregressive moving average'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Vector autoregressive moving average.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Dong, Juntao. "Reinforcement Learning for Multiple Time Series: Forex Trading Application." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613745680121778.
Full text
2
Akin, Serdar. "Do Riksbanken produce unbiased forecast of the inflation rate? : and can it be improved?" Thesis, Stockholms universitet, Nationalekonomiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-58708.
Full textAbstract:
The focus of this paper is to evaluate if forecast produced by the Central Bank of Sweden (Riksbanken) for the 12 month change in the consumer price index is unbiased? Results shows that for shorter horizons (h < 12) the mean forecast error is unbiased but for longer horizons its negatively biased when inference is done by Maximum entropy bootstrap technique. Can the unbiasedness be improved by strict ap- pliance to econometric methodology? Forecasting with a linear univariate model (seasonal ARIMA) and a multivariate model Vector Error Correction model (VECM) shows that when controlling for the presence of structural breaks VECM outperforms both prediction produced Riksbanken and ARIMA. However Riksbanken had the best precision in their forecast, estimated as MSFE
3
Ziedzor, Reginald. "GENERALIZED AUTOREGRESSIVE MOVING AVERAGE MODELS." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2198.
Full text
4
Alj, Abdelkamel. "Contribution to the estimation of VARMA models with time-dependent coefficients." Doctoral thesis, Universite Libre de Bruxelles, 2012. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209651.
Full textAbstract:
Dans cette thèse, nous étudions l’estimation de modèles autorégressif-moyenne mobilevectoriels ou VARMA, `a coefficients dépendant du temps, et avec une matrice de covariance
des innovations dépendant du temps. Ces modèles sont appel´es tdVARMA. Les éléments
des matrices des coefficients et de la matrice de covariance sont des fonctions déterministes
du temps dépendant d’un petit nombre de paramètres. Une première partie de la thèse
est consacrée à l’étude des propriétés asymptotiques de l’estimateur du quasi-maximum
de vraisemblance gaussienne. La convergence presque sûre et la normalité asymptotique
de cet estimateur sont démontrées sous certaine hypothèses vérifiables, dans le cas o`u les
coefficients dépendent du temps t mais pas de la taille des séries n. Avant cela nous considérons les propriétés asymptotiques des estimateurs de modèles non-stationnaires assez
généraux, pour une fonction de pénalité générale. Nous passons ensuite à l’application de
ces théorèmes en considérant que la fonction de pénalité est la fonction de vraisemblance
gaussienne (Chapitre 2). L’étude du comportement asymptotique de l’estimateur lorsque
les coefficients du modèle dépendent du temps t et aussi de n fait l’objet du Chapitre 3.
Dans ce cas, nous utilisons une loi faible des grands nombres et un théorème central limite
pour des tableaux de différences de martingales. Ensuite, nous présentons des conditions
qui assurent la consistance faible et la normalité asymptotique. Les principaux
résultats asymptotiques sont illustrés par des expériences de simulation et des exemples
dans la littérature. La deuxième partie de cette thèse est consacrée à un algorithme qui nous
permet d’évaluer la fonction de vraisemblance exacte d’un processus tdVARMA d’ordre (p, q) gaussien. Notre algorithme est basé sur la factorisation de Cholesky d’une matrice
bande partitionnée. Le point de départ est une généralisation au cas multivarié de Mélard
(1982) pour évaluer la fonction de vraisemblance exacte d’un modèle ARMA(p, q) univarié. Aussi, nous utilisons quelques résultats de Jonasson et Ferrando (2008) ainsi que les programmes Matlab de Jonasson (2008) dans le cadre d’une fonction de vraisemblance
gaussienne de modèles VARMA à coefficients constants. Par ailleurs, nous déduisons que
le nombre d’opérations requis pour l’évaluation de la fonction de vraisemblance en fonction de p, q et n est approximativement le double par rapport à un modèle VARMA à coefficients
constants. L’implémentation de cet algorithme a été testée en comparant ses résultats avec
d’autres programmes et logiciels très connus. L’utilisation des modèles VARMA à coefficients
dépendant du temps apparaît particulièrement adaptée pour la dynamique de quelques
séries financières en mettant en évidence l’existence de la dépendance des paramètres en
fonction du temps.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
5
Akgun, Burcin. "Identification Of Periodic Autoregressive Moving Average Models." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1083682/index.pdf.
Full textAbstract:
In this thesis, identification of periodically varying orders of univariate
Periodic Autoregressive Moving-Average (PARMA) processes is mainly studied.
The identification of the varying orders of PARMA process is carried
out by generalizing the well-known Box-Jenkins techniques to a seasonwise
manner. The identification of pure periodic moving-average (PMA) and pure
periodic autoregressive (PAR) models are considered only. For PARMA model
identification, the Periodic Autocorrelation Function (PeACF) and Periodic Partial
Autocorrelation Function (PePACF), which play the same role as their ARMA
counterparts, are employed.
For parameter estimation, which is considered only to refine model
identification, the conditional least squares estimation (LSE) method is used
which is applicable to PAR models. Estimation becomes very complicated,
difficult and may give unsatisfactory results when a moving-average (MA)
component exists in the model. On account of overcoming this difficulty,
seasons following PMA processes are tried to be modeled as PAR processes
with reasonable orders in order to employ LSE. Diagnostic checking, through
residuals of the fitted model, is also performed stating its reasons and methods.
The last part of the study demonstrates application of identification
techniques through analysis of two seasonal hydrologic time series, which
consist of average monthly streamflows. For this purpose, computer programs
were developed specially for PARMA model identification.
6
Chong, Ching Yee. "Portmanteau testing for nonstationary autoregressive moving-average models /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?MATH%202003%20CHONG.
Full textAbstract:
Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.Includes bibliographical references (leaves 37-39). Also available in electronic version. Access restricted to campus users.
7
Mohammadipour, Maryam. "Intermittent demand forecasting with integer autoregressive moving average models." Thesis, Bucks New University, 2009. http://bucks.collections.crest.ac.uk/9586/.
Full textAbstract:
This PhD thesis focuses on using time series models for counts in modelling and forecasting a special type of count series called intermittent series. An intermittent series is a series of non-negative integer values with some zero values. Such series occur in many areas including inventory control of spare parts. Various methods have been developed for intermittent demand forecasting with Croston’s method being the most widely used. Some studies focus on finding a model underlying Croston’s method. With none of these studies being successful in demonstrating an underlying model for which Croston’s method is optimal, the focus should now shift towards stationary models for intermittent demand forecasting. This thesis explores the application of a class of models for count data called the Integer Autoregressive Moving Average (INARMA) models. INARMA models have had applications in different areas such as medical science and economics, but this is the first attempt to use such a model-based method to forecast intermittent demand. In this PhD research, we first fill some gaps in the INARMA literature by finding the unconditional variance and the autocorrelation function of the general INARMA(p,q) model. The conditional expected value of the aggregated process over lead time is also obtained to be used as a lead time forecast. The accuracy of h-step-ahead and lead time INARMA forecasts are then compared to those obtained by benchmark methods of Croston, Syntetos-Boylan Approximation (SBA) and Shale-Boylan-Johnston (SBJ). The results of the simulation suggest that in the presence of a high autocorrelation in data, INARMA yields much more accurate one-step ahead forecasts than benchmark methods. The degree of improvement increases for longer data histories. It has been shown that instead of identification of the autoregressive and moving average order of the INARMA model, the most general model among the possible models can be used for forecasting. This is especially useful for short history and high autocorrelation in data. The findings of the thesis have been tested on two real data sets: (i) Royal Air Force (RAF) demand history of 16,000 SKUs and (ii) 3,000 series of intermittent demand from the automotive industry. The results show that for sparse data with long history, there is a substantial improvement in using INARMA over the benchmarks in terms of Mean Square Error (MSE) and Mean Absolute Scaled Error (MASE) for the one-step ahead forecasts. However, for series with short history the improvement is narrower. The improvement is greater for h-step ahead forecasts. The results also confirm the superiority of INARMA over the benchmark methods for lead time forecasts.
8
SCHER, Vinícius Teodoro. "Portmanteau testing inference in beta autoregressive moving average models." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/26891.
Full textAbstract:
Submitted by Pedro Barros (pedro.silvabarros@ufpe.br) on 2018-09-21T20:16:54Z
No. of bitstreams: 2
license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5)
DISSERTAÇÃO Vinícius Teodoro Scher.pdf: 902853 bytes, checksum: 8ce4def07864471ce7717ce6d128d086 (MD5)Approved for entry into archive by Alice Araujo (alice.caraujo@ufpe.br) on 2018-09-24T20:48:15Z (GMT) No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Vinícius Teodoro Scher.pdf: 902853 bytes, checksum: 8ce4def07864471ce7717ce6d128d086 (MD5)
Made available in DSpace on 2018-09-24T20:48:15Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Vinícius Teodoro Scher.pdf: 902853 bytes, checksum: 8ce4def07864471ce7717ce6d128d086 (MD5) Previous issue date: 2017-08-02
CAPES
The class of beta autoregressive moving average (bARMA) models is useful for modeling time series data that assume values in the standard unit interval, such as rates and proportions. This thesis is composed of two main and independent chapters. In the first part, we consider portmanteau testing inference in the class of bARMA models. To that end, we use tests that have been developed for Gaussian models, such as the Ljung and Box, Monti, Dufour and Roy, Kwan and Sim, and Lin and McLeod tests. We also consider bootstrap variants of the Ljung and Box, Monti, Dufour and Roy, and Kwan and Sim tests. Moreover, we propose two new test statistics which, like the Monti statistic, are based on residual partial autocorrelations. Additionally, we present and discuss results from Monte Carlo simulations and an empirical application. The second part of the thesis focuses on the recursive nature of bARMA loglikelihood derivatives under moving average dynamics. We provide closed form expressions for the relevant derivatives by considering errors in the predictor scale.
A classe de modelos beta autorregressivos de médias móveis (bARMA) é útil para modelar dados que assumem valores no intervalo unitário padrão, como taxas e proporções. A presente dissertação tem como tema tal classe de models e é composta por dois capítulos principais e independentes. Na primeira parte, consideramos inferências baseadas em testes portmanteau na classe de modelos bARMA. Para tanto, utilizamos testes que foram desenvolvidos para modelos gaussianos, como os testes de Ljung e Box, Monti, Dufour e Roy, Kwan e Sim, e Lin e McLeod. Também consideramos variantes bootstrap dos testes de Ljung e Box, Monti, Dufour e Roy and Kwan e Sim. Adicionalmente, propomos duas novas estatísticas de testes que, tal qual a estatística de Monti, são baseadas em autocorrelações parciais dos resíduos. Apresentamos e discutimos resultados de simulações de Monte Carlo e uma aplicação empírica. A segunda parte da dissertação aborda a natureza recursiva das derivadas da função de log-verossimilhança bARMA sob dinâmica de médias móveis. Nós fornecemos expressões em forma fechada para as derivadas relevantes considerando erros na escala do preditor.
9
Leser, Christoph. "On stationary and nonstationary fatigue load modeling using autoregressive moving average (ARMA) models." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/29319.
Full textAbstract:
The concise description of one- and multidimensional stationary and non stationary vehicle loading histories for fatigue analysis using stochastic process theory is presented in this study. The load history is considered to have stationary random and nonstationary mean and variance content. The stationary variations are represented by a class of time series referred to as Autoregressive Moving Average (ARMA) models, while a Fourier series is used to account for the variation of the mean and variance. Due to the use of random phase angles in the Fourier series, an ensemble of mean and variance variations is obtained. The methods of nonparametric statistics are used to determine the success of the modeling of nonstationarity. Justification of the method is obtained through comparison of rainflow cycle distributions and resulting fatigue lives of original and simulated loadings. Due to the relatively small number of Fourier coefficients needed together with the use of ARMA models, a concise description of complex loadings is achieved. The overall frequency content and sequential information of the load history is statistically preserved. An ensemble of load histories can be constructed on-line with minimal computer storage capacity as used in testing equipment. The method can be used in a diversity of fields where a concise representation of random loadings is desired.Ph. D.
10
Hanh, Nguyen T. "Lasso for Autoregressive and Moving Average Coeffients via Residuals of Unobservable Time Series." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo154471227291601.
Full text
11
Ehlers, Ricardo Sandes. "Bayesian model discrimination for time series and state space models." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/843599/.
Full textAbstract:
In this thesis, a Bayesian approach is adopted to handle parameter estimation and model uncertainty in autoregressive moving average (ARMA) time series models and dynamic linear models (DLM). Bayesian model uncertainty is handled in a parametric fashion through the use of posterior model probabilities computed via Markov chain Monte Carlo (MCMC) simulation techniques. Attention is focused on reversible jump Markov chain Monte Carlo (RJMCMC) samplers, which can move between models of different dimensions, to address the problem of model order uncertainty and strategies for proposing efficient sampling schemes in autoregressive moving average time series models and dynamic linear models are developed. The general problem of assessing convergence of the sampler in a dimension-changing context is addressed by computing estimates of the probabilities of moving to higher and lower dimensional spaces. Graphical and numerical techniques are used to compare different updating schemes. The methodology is illustrated by applying it to both simulated and real data sets and the results for the Bayesian model selection and parameter estimation procedures are compared with the classical model selection criteria and maximum likelihood estimation.
12
Niebuhr, Tobias [Verfasser], and J. P. [Akademischer Betreuer] Kreiß. "Bootstrap for continuous-time autoregressive moving average processes / Tobias Niebuhr ; Betreuer: J.-P. Kreiß." Braunschweig : Technische Universität Braunschweig, 2014. http://d-nb.info/1175820237/34.
Full text
13
Albarracin, Orlando Yesid Esparza. "Generalized autoregressive and moving average models: control charts, multicollinearity, and a new modified model." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-21112017-184544/.
Full textAbstract:
Recently, in the health surveillance area, control charts have been proposed to decide if the morbidity or mortality of a specific disease reached an epidemic level. This thesis is composed by 3 papers. In the first two papers, CUSUM and EWMA control charts were proposed to monitor count time series with seasonal and trend effects using the Generalized Autoregressive and Moving Average models (GARMA), instead of the independent Generalized Linear Model (GLM) as it is usually used in practice. Different statistics based on transformations, for variables that follow a Negative Binomial distribution, were used in these control charts. In the second paper, two new statistics were proposed based on the ratio of log-likelihood function. Different scenarios describing disease profiles were considered to evaluate the effect of omission of serial correlation in EWMA and CUSUM control charts. The performance of CUSUM and EWMA charts when the serial correlation is neglected in the regression model was measure in terms of average run length (ARL). In summary, when the autocorrelation is neglected, fitting a pure GLM instead of a GARMA model will lead to an increase of false alarms. However, no statistics among the tested ones seem to be robust, in a sense to produce the smallest increase of false alarms in all scenarios. In general, all monitored statistics presented a smaller ARL_0 for higher values of autocorrelation. \\\\ In the last paper, the GARMA models (p, q) with p and q simultaneously different from zero were studied since that two features were observed in practice. One is the multicollinearity, which may lead to a non-convergence of the maximum likelihood, using iteratively reweighted least squares. The second is the inclusion of the same lagged observations into the autoregressive and moving average components confounding the interpretation of the parameters. In a general sense, simulation studies show that the modified model provide estimators closer to the parameters and offer confidence intervals with higher coverage percentage than obtained with the GARMA model, but some restrictions in the parametric space are imposed to guarantee the stationarity of the process. Also, a real data analysis illustrate the GARMA-M fit for daily hospilatization rates of elderly people due to respiratory diseases from October 2012 to April 2015 in São Paulo city, Brazil.Recentemente, no campo da saúde, gráficos de controle têm sido propostos para monitorar a morbidade ou a mortalidade decorrentes de doenças. Este trabalho está composto por três artigos. Nos dois primeiros artigos, gráficos de controle CUSUM e EWMA foram propostos para monitorar séries temporais de contagens com efeitos sazonais e de tendência usando os modelos Generalized autoregressive and moving average models (GARMA), em vez dos modelos lineares generalizados (GLM), como usualmente são utilizados na prática. Diferentes estatísticas baseadas em transformações, para variávies que seguem uma distribuição Binomial Negativa, foram usadas nestes gráficos de controle. No segundo artigo foram propostas duas novas estatísticas baseadas na razão da função de log-verossimilhança. Diferentes cenários que descrevem perfis de doenças foram considerados para avaliar o efeito da omissão da correlação serial nesses gráficos de controle. Este impacto foi medido em termos do Average Run Lenght (ARL). Notou-se que a negligência da correlação serial induz um aumento de falsos alarmes. Em geral, todas as estatísticas monitoradas apresentaram menores valores de ARL_0 para maiores valores de autocorrelação. No entanto, nenhuma estatística entre as consideradas mostrou ser mais robusta, no sentido de produzir o menor aumento de falsos alarmes nos cenários considerados. No último artigo, foram estudados os modelos GARMA (p, q) com p e q simultaneamente diferentes de zero, uma vez que duas características foram observadas na prática. A primeira é a presença de multicolinearidade, que induz à não-convergência do método de máxima verossimilhança usando mínimos quadrados ponderados reiterados. A segunda é a inclusão dos mesmos termos defasados nos componentes autorregressivos e de médias móveis. Um modelo modificado, GARMA-M, foi apresentado para lidar com a multicolinearidade e melhorar a interpretação dos parâmetros. Em sentido geral, estudos de simulação mostraram que o modelo modificado fornece estimativas mais próximas dos parâmetros e intervalos de confiança com uma cobertura percentual maior do que a obtida nos modelos GARMA. No entanto, algumas restrições no espaço paramétrico são impostas para garantir a estacionariedade do processo. Por último, uma análise de dados reais ilustra o ajuste do modelo GARMA-M para o número de internações diárias de idosos devido a doenças respiratórias de outubro de 2012 a abril de 2015 na cidade de São Paulo, Brasil.
14
Lee, Kian Lam. "Nonlinear time series modelling and prediction using polynomial and radial basis function expansions." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246940.
Full text
15
Scholz, Markus [Verfasser], and V. [Akademischer Betreuer] Fasen-Hartmann. "Estimation of Cointegrated Multivariate Continuous-Time Autoregressive Moving Average Processes / Markus Scholz. Betreuer: V. Fasen-Hartmann." Karlsruhe : KIT-Bibliothek, 2016. http://d-nb.info/1112224866/34.
Full text
16
Kettner, Marvin [Verfasser], Frank [Akademischer Betreuer] Aurzada, and Vitali [Akademischer Betreuer] Wachtel. "Persistence exponents via perturbation theory : autoregressive and moving average processes / Marvin Kettner ; Frank Aurzada, Vitali Wachtel." Darmstadt : Universitäts- und Landesbibliothek, 2021. http://d-nb.info/1228537518/34.
Full text
17
Ankrah, Samuel K. O. "A case study of short-run forecasting of commodity prices : an application of autoregressive integrated moving average models." Thesis, McGill University, 1991. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=61112.
Full textAbstract:
That Ghana derives her foreign exchange earnings mainly from cocoa and gold exports cannot be over emphasised. There is therefore the need to forecast these commodities prices as accurately as possible for proper planning and execution of major policies, since the prices have been notoriously volatile during the past two decades and attempts to stabilize especially the price of the beans (which contributes about 60% of the country's foreign exchange) through the system of buffer stock and export restrictions have not been successful. In this regard, autoregressive integrated moving averages models are built and used to generate short run forecasts for the beans and the precious metal price series. These models are simple to build and appear not only to describe the behaviour of the series but provide good forecasts of the prices.
18
Lee, Hyung-Jin. "Regional forecasting of hydrologic parameters." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178223662.
Full text
19
Van, Heerden Petrus Marthinus Stephanus. "The relationship between the forward– and the realized spot exchange rate in South Africa / Petrus Marthinus Stephanus van Heerden." Thesis, North-West University, 2010. http://hdl.handle.net/10394/4511.
Full textAbstract:
The inability to effectively hedge against unfavourable exchange rate movements, using the
current forward exchange rate as the only guideline, is a key inhibiting factor of international
trade. Market participants use the current forward exchange rate quoted in the market to make
decisions regarding future exchange rate changes. However, the current forward exchange rate
is not solely determined by the interaction of demand and supply, but is also a mechanistic
estimation, which is based on the current spot exchange rate and the carry cost of the
transaction. Results of various studies, including this study, demonstrated that the current
forward exchange rate differs substantially from the realized future spot exchange rate. This
phenomenon is known as the exchange rate puzzle.
This study contributes to the dynamics of modelling exchange rate theories by developing an
exchange rate model that has the ability to explain the realized future spot exchange rate and
the exchange rate puzzle. The exchange rate model is based only on current (time t) economic
fundamentals and includes an alternative approach of incorporating the impact of the interaction
of two international financial markets into the model. This study derived a unique exchange rate
model, which proves that the exchange rate puzzle is a pseudo problem. The pseudo problem
is based on the generally excepted fallacy that current non–stationary, level time series data
cannot be used to model exchange rate theories, because of the incorrect assumption that all
the available econometric methods yield statistically insignificant results due to spurious
regressions. Empirical evidence conclusively shows that using non–stationary, level time series
data of current economic fundamentals can statistically significantly explain the realized future
spot exchange rate and, therefore, that the exchange rate puzzle can be solved.
This model will give market participants in the foreign exchange market a better indication of
expected future exchange rates, which will considerably reduce the dependence on the
mechanistically derived forward points. The newly derived exchange rate model will also have an influence on the demand and supply of forward exchange, resulting in forward points that are
a more accurate prediction of the realized future exchange rate.Thesis (Ph.D. (Risk management))--North-West University, Potchefstroom Campus, 2011.
20
Avventi, Enrico, Anders Lindquist, and Bo Wahlberg. "ARMA Identification of Graphical Models." KTH, Optimeringslära och systemteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-39065.
Full textAbstract:
Consider a Gaussian stationary stochastic vector process with the property that designated pairs of components are conditionally independent given the rest of the components. Such processes can be represented on a graph where the components are nodes and the lack of a connecting link between two nodes signifies conditional independence. This leads to a sparsity pattern in the inverse of the matrix-valued spectral density. Such graphical models find applications in speech, bioinformatics, image processing, econometrics and many other fields, where the problem to fit an autoregressive (AR) model to such a process has been considered. In this paper we take this problem one step further, namely to fit an autoregressive moving-average (ARMA) model to the same data. We develop a theoretical framework and an optimization procedure which also spreads further light on previous approaches and results. This procedure is then applied to the identification problem of estimating the ARMA parameters as well as the topology of the graph from statistical data.Updated from "Preprint" to "Article" QC 20130627
21
Angot, Christophe. "La dynamique de la motivation situationnelle." Limoges, 2013. https://aurore.unilim.fr/theses/nxfile/default/9168bb1e-433c-4b16-993e-19abf632d541/blobholder:0/2013LIMO4047.pdf.
Full textAbstract:
Le modèle hiérarchique de la motivation intrinsèque et extrinsèque de Vallerand (1997) soumet l’idée que les motivations seraient définies selon trois niveaux de généralité. Le niveau situationnel réfère à la motivation à l’instant « t », celle qui nous habite durant la pratique même d’une activité. Le niveau contextuel renvoie aux motivations de l’individu envers un contexte de vie (e. G. , activité physique, éducation). Enfin le niveau global représente l’orientation motivationnelle générale d’un individu à interagir avec son environnement. Nous nous sommes plus particulièrement focalisés sur le niveau situationnel puisqu’il permet notamment de distinguer les évolutions de la motivation au cours d’une même activité. Notre première étude (i. E. , chapitre 3) consiste à montrer que la motivation intrinsèque situationnelle est un construit dynamique. Les études deux, trois et quatre (cf. Chapitre 4), consistent à élaborer, valider et appliquer un nouvel outil de mesure de la motivation situationnelle. Cet outil (i. E. , SiMS4) est un questionnaire de type « papier-crayon » composé de quatre items (i. E. , motivation intrinsèque, deux types de motivations extrinsèques et l’amotivation) qui permet aux sujets de s’autoévaluer plusieurs fois pendant la réalisation d’une tâche. Notre dernière étude (cf. Chapitre 5), est une nouvelle application de notre outil de mesure dans le milieu scolaire. Nous montrons que la motivation situationnelle des élèves est à la croisée entre un mécanisme de préservation et de flexibilité. Ce mécanisme rend possible une évolution continue plus ou moins importante de la motivation pendant la situation scolaireThe hierarchical model of intrinsic and extrinsic motivation (Vallerand, 1997) suggested that motivation exists at three levels of generality. The situational level refers to the motivation that individuals experience when they are currently engaging in an activity. The contextual level refers to individuals’ usual motivational orientation toward a specific context (e. G. , physical activity, education). Finally global motivation represents general guidelines to the causality of a person within the meaning described by Deci and Ryan (1985). We especially focused on the situational level since it allows us to distinguish between changes in motivation during the same activity. Our first study (i. E. , chapter 3) aims at showing that situational intrinsic motivation is a dynamic construct. Studies two, three and four (i. E. , chapter 4) are conducted to develop, validate and apply a new tool for measuring situational motivation. This tool (i. E. , SiMS4) is a "paper and pencil" questionnaire-type with four items (i. E. , intrinsic motivation, two types of extrinsic motivation, and amotivation) which allows subjects to self-evaluate several times when performing a task. Our last study (i. E. , chapter 5) is a new application of our tool in schools. We show that the students’ situational motivation vary between a mechanism for preserving and flexibility. This mechanism makes possible a more or less important continuous evolution of the motivation during the school situation
22
Biz, Guilherme. "Simulações de pesos espaciais para o modelo STARMA e aplicações." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-15092014-123217/.
Full textAbstract:
A modelagem de processos espaço-temporais é de suma importância para dados climatológicos, visto que o clima sofre influência temporal e espacial. A classe de modelos STARMA, autorregressivo e de médias móveis espaço-temporal, adequa-se a esses processos, porém, não há, na literatura, um estudo sobre o melhor método para quantificar a dependência espacial, e não é sabido se há uma diferença entre os métodos para esses modelos. Logo, neste trabalho, é realizado um estudo de simulações do modelo STAR, utilizando-se diferentes formas para obter os pesos espaciais. Após concluir as simulações é realizado o ajuste de um modelo STARIMA para um conjunto de dados de médias mensais de temperaturas mínimas diárias coletadas em uma mesorregião localizada no Oeste do Estado do Paraná. Este trabalho é separado em dois artigos e ambos são realizados utilizando-se o programa R. O primeiro é o estudo de simulações, chegando-se à conclusão de que o método para determinar a dependência espacial interfere no resultado da modelagem e depende da região em estudo. No segundo artigo, conclui-se que o inverso da distância é a melhor opção para a matriz de pesos e um modelo STARIMA sazonal tem o melhor ajuste para o conjunto de dados em questão.Process modeling spatio-temporal is of great importance for climatological data, once that the climate undergoes spatial and temporal influence. The class of models STARMA, autoregressive models and spatio-temporal moving averages, are suitable to the these processes, however, for these models, there is not a study about the best method to quantify the spatial dependence, and/or it is not known whether there is a difference between the methods for these models. In this thesis, a study simulations of the STAR model using different forms for the spatial weights is performed. After the simulation procedure, the STARIMA model is fitted to the real dataset of monthly mean daily minimum temperatures collected in a mesoregion located to the west of the state of Paraná. This thesis is separated into two papers and both are performed using the statistical software R. The first one is the simulation study that concludes that the method for determining the spatial dependence interferes with results of the modeling and depends on the region under study. In the second paper, it is concluded that the inverse distance is the best option for the weight matrix and a seasonal STARIMA model has the best fit for the data set.
23
Stitou, Adnane. "SARIMA Short to Medium-Term Forecasting and Stochastic Simulation of Streamflow, Water Levels and Sediments Time Series from the HYDAT Database." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39785.
Full textAbstract:
This study aims to investigate short-to-medium forecasting and simulation of streamflow, water levels, and sediments in Canada using Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models. The methodology can account for linear trends in the time series that may result from climate and environmental changes. A Universal Canadian forecast Application using python web interface was developed to generate short-term forecasts using SARIMA. The Akaike information criteria was used as performance criteria for generating efficient SARIMA models. The developed models were validated by analyzing the residuals. Several stations from the Canadian Hydrometric Database (HYDAT) displaying a linear upward or downward trend were identified to validate the methodology. Trends were detected using the Man-Kendall test.
The Nash-Sutcliffe efficiency coefficients (Nash ad Sutcliffe, 1970) of the developed models indicate that they are acceptable. The models can be used for short term (1 to 7 days) and medium-term (7 days to six months) forecasting of streamflow, water levels and sediments at all Canadian hydrometric stations. Such a forecast can be used for water resources management and help mitigate the effects of floods and droughts. The models can also be used to generate long time-series that can be used to test the performance of water resources systems.
Finally, we have automated the process of analysis, model-building and forecasting streamflow, water levels, and sediments by building a python-based application easily extendable and user-friendly. Therefore, automating the SARIMA calibration and forecasting process for all Canadian stations for the HYDAT database will prove to be a very useful tool for decision-makers and other entities in the field of hydrological study.
24
Tanriverdi, Gunes. "Arma Model Based Clutter Estimation And Its Effect On Clutter Supression Algorithms." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614360/index.pdf.
Full textAbstract:
Radar signal processing techniques aim to suppress clutter to enable target detection. Many clutter suppression techniques have been developed to improve the detection performance in literature. Among these methods, the most widely known is MTI plus coherent integrator, which gives sufficient radar performance in various scenarios. However, when the correlation coefficient of clutter is small or the spectral separation between the target and clutter is small, classical approaches to clutter suppression fall short.
In this study, we consider the ARMA spectral estimation performance in sea clutter modelled by compound K-distribution through Monte Carlo simulations. The method is applied for varying conditions of clutter spikiness and auto correlation sequences (ACS) depending on the radar operation. The performance of clutter suppression using ARMA spectral estimator, which will be called ARMA-CS in this work, is analyzed under varying ARMA model orders.
To compare the clutter suppression of ARMA-CS with that of conventional methods, we use improvement factor (IF) which is the ratio between the output Signal to Interference Ratio (SIR) and input SIR as performance measure. In all cases, the performance of ARMA-CS method is better than conventional clutter suppression methods when the correlation among clutter samples is small or the spectral separation between target and clutter is small.
25
Alrabady, Linda Antoun Yousef. "An online-integrated condition monitoring and prognostics framework for rotating equipment." Thesis, Cranfield University, 2014. http://dspace.lib.cranfield.ac.uk/handle/1826/9204.
Full textAbstract:
Detecting abnormal operating conditions, which will lead to faults developing
later, has important economic implications for industries trying to meet their
performance and production goals. It is unacceptable to wait for failures that
have potential safety, environmental and financial consequences. Moving from
a “reactive” strategy to a “proactive” strategy can improve critical equipment
reliability and availability while constraining maintenance costs, reducing
production deferrals, decreasing the need for spare parts. Once the fault
initiates, predicting its progression and deterioration can enable timely
interventions without risk to personnel safety or to equipment integrity.
This work presents an online-integrated condition monitoring and prognostics
framework that addresses the above issues holistically. The proposed
framework aligns fully with ISO 17359:2011 and derives from the I-P and P-F
curve. Depending upon the running state of machine with respect to its I-P and
P-F curve an algorithm will do one of the following:
(1) Predict the ideal behaviour and any departure from the normal operating
envelope using a combination of Evolving Clustering Method (ECM), a
normalised fuzzy weighted distance and tracking signal method.
(2) Identify the cause of the departure through an automated diagnostics
system using a modified version of ECM for classification.
(3) Predict the short-term progression of fault using a modified version of the
Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), called here
MDENFIS and a tracking signal method.
(4) Predict the long term progression of fault (Prognostics) using a combination
of Autoregressive Integrated Moving Average (ARIMA)- Empirical Mode
Decomposition (EMD) for predicting the future input values and MDENFIS for
predicting the long term progression of fault (output).
The proposed model was tested and compared against other models in the
literature using benchmarks and field data. This work demonstrates four
noticeable improvements over previous methods:
(1) Enhanced testing prediction accuracy, (2) comparable processing time if not
better, (3) the ability to detect sudden changes in the process and finally (4) the
ability to identify and isolate the problem source with high accuracy.
26
Claudino, Joana Filipa Caetano. "Intelligent system for time series pattern identification and prediction." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/21036.
Full textAbstract:
Mestrado em Gestão de Sistemas de InformaçãoOs crescentes volumes de dados representam uma fonte de informação potencialmente valiosa para as empresas, mas também implicam desafios nunca antes enfrentados. Apesar da sua complexidade intrínseca, as séries temporais são um tipo de dados notavelmente relevantes para o contexto empresarial, especialmente para tarefas preditivas. Os modelos Autorregressivos Integrados de Médias Móveis (ARIMA), têm sido a abordagem mais popular para tais tarefas, porém, não estão preparados para lidar com as cada vez mais comuns séries temporais de maior dimensão ou granularidade. Assim, novas tendências de investigação envolvem a aplicação de modelos orientados a dados, como Redes Neuronais Recorrentes (RNNs), à previsão. Dada a dificuldade da previsão de séries temporais e a necessidade de ferramentas aprimoradas, o objetivo deste projeto foi a implementação dos modelos clássicos ARIMA e as arquiteturas RNN mais proeminentes, de forma automática, e o posterior uso desses modelos como base para o desenvolvimento de um sistema modular capaz de apoiar o utilizador em todo o processo de previsão. Design science research foi a abordagem metodológica adotada para alcançar os objetivos propostos e envolveu, para além da identificação dos objetivos, uma revisão aprofundada da literatura que viria a servir de suporte teórico à etapa seguinte, designadamente a execução do projeto e findou com a avaliação meticulosa do artefacto produzido. No geral todos os objetivos propostos foram alcançados, sendo os principais contributos do projeto o próprio sistema desenvolvido devido à sua utilidade prática e ainda algumas evidências empíricas que apoiam a aplicabilidade das RNNs à previsão de séries temporais.
The current growing volumes of data present a source of potentially valuable information for companies, but they also pose new challenges never faced before. Despite their intrinsic complexity, time series are a notably relevant kind of data in the entrepreneurial context, especially regarding prediction tasks. The Autoregressive Integrated Moving Average (ARIMA) models have been the most popular approach for such tasks, but they do not scale well to bigger and more granular time series which are becoming increasingly common. Hence, newer research trends involve the application of data-driven models, such as Recurrent Neural Networks (RNNs), to forecasting. Therefore, given the difficulty of time series prediction and the need for improved tools, the purpose of this project was to implement the classical ARIMA models and the most prominent RNN architectures in an automated fashion and posteriorly to use such models as foundation for the development of a modular system capable of supporting the common user along the entire forecasting process. Design science research was the adopted methodology to achieve the proposed goals and it comprised the activities of goal definition, followed by a thorough literature review aimed at providing the theoretical background necessary to the subsequent step that involved the actual project execution and, finally, the careful evaluation of the produced artifact. In general, each the established goals were accomplished, and the main contributions of the project were the developed system itself due to its practical usefulness along with some empirical evidence supporting the suitability of RNNs to time series forecasting.
info:eu-repo/semantics/publishedVersion
27
Laraqui-Houssei͏̈ni, Fouzia. "Identification du degré d'un processus autorégressif en présence de valeurs aberrantes." Grenoble 1, 1989. http://www.theses.fr/1989GRE10079.
Full textAbstract:
On décrit des méthodes d'identification de modèles de processus autorégressifs-moyennes mobiles et on présente les modèles de processus contamine. L'effet de la présence de valeurs aberrantes est analyse, sur l'autocorrélation et les méthodes d'identification. Un estimateur robuste de la fonction d'autocorrélation partielle est proposé, en vue de l'identification du degré d'un processus autorégressif. Un coefficient d'autocorrélation partielle est défini dans le cadre non paramétrique, base sur les statistiques de rang
28
Dall'Olio, Lorenzo. "Estimation of biological vascular ageing via photoplethysmography: a comparison between statistical learning and deep learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21687/.
Full textAbstract:
This work aims to exploit the biological ageing phenomena which affects human blood vessels. The analysis is performed starting from a database of photoplethysmographic signals acquired through smartphones. The further step involves a preprocessing phase, where the signals are detrended using a central moving average filter, demoduled using the envelope of the analytic signal obtained from the Hilbert transform, denoised using the central moving average filter over the envelope. After the preprocessing we compared two different approaches. The first one regards Statistical Learning, which involves feature extraction and selection through the usage of statistics and machine learning algorithms. This in order to perform a classification supervised task over the chronological age of the individual, which is used as a proxy for healthy/non healthy vascular ageing. The second one regards Deep Learning, which involves the realisation of a convolutional neural network to perform the same task, but avoiding the feature extraction/selection step and so possible bias introduced by such phases. Doing so we obtained comparable outcomes in terms of area under the curve metrics from a 12 layers ResNet convolutional network and a support vector machine using just covariates together with a couple of extracted features, acquiring clues regarding the possible usage of such features as biomarkers for the vascular ageing process. The two mentioned features can be related with increasing arterial stiffness and increasing signal randomness due to ageing.
29
Kratz, Marie. "Some contributions in probability and statistics of extremes." Habilitation à diriger des recherches, Université Panthéon-Sorbonne - Paris I, 2005. http://tel.archives-ouvertes.fr/tel-00239329.
Full text
30
Grahl, Paulo Gustavo de Sampaio. "Essays on Spatial Econometrics." reponame:Repositório Institucional do FGV, 2012. http://hdl.handle.net/10438/11268.
Full textAbstract:
Submitted by Paulo Gustavo Grahl (pgrahl@fgvmail.br) on 2013-10-18T05:32:44Z
No. of bitstreams: 1
DoutoradoPG_final.pdf: 23501670 bytes, checksum: 55b15051b9acc69ac74e639efe776fae (MD5)Approved for entry into archive by ÁUREA CORRÊA DA FONSECA CORRÊA DA FONSECA (aurea.fonseca@fgv.br) on 2013-10-28T18:22:53Z (GMT) No. of bitstreams: 1 DoutoradoPG_final.pdf: 23501670 bytes, checksum: 55b15051b9acc69ac74e639efe776fae (MD5)
Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2013-10-29T18:24:15Z (GMT) No. of bitstreams: 1 DoutoradoPG_final.pdf: 23501670 bytes, checksum: 55b15051b9acc69ac74e639efe776fae (MD5)
Made available in DSpace on 2013-10-29T18:25:35Z (GMT). No. of bitstreams: 1 DoutoradoPG_final.pdf: 23501670 bytes, checksum: 55b15051b9acc69ac74e639efe776fae (MD5) Previous issue date: 2012-12-22
Esta dissertação concentra-se nos processos estocásticos espaciais definidos em um reticulado, os chamados modelos do tipo Cliff & Ord. Minha contribuição nesta tese consiste em utilizar aproximações de Edgeworth e saddlepoint para investigar as propriedades em amostras finitas do teste para detectar a presença de dependência espacial em modelos SAR (autoregressivo espacial), e propor uma nova classe de modelos econométricos espaciais na qual os parâmetros que afetam a estrutura da média são distintos dos parâmetros presentes na estrutura da variância do processo. Isto permite uma interpretação mais clara dos parâmetros do modelo, além de generalizar uma proposta de taxonomia feita por Anselin (2003). Eu proponho um estimador para os parâmetros do modelo e derivo a distribuição assintótica do estimador. O modelo sugerido na dissertação fornece uma interpretação interessante ao modelo SARAR, bastante comum na literatura. A investigação das propriedades em amostras finitas dos testes expande com relação a literatura permitindo que a matriz de vizinhança do processo espacial seja uma função não-linear do parâmetro de dependência espacial. A utilização de aproximações ao invés de simulações (mais comum na literatura), permite uma maneira fácil de comparar as propriedades dos testes com diferentes matrizes de vizinhança e corrigir o tamanho ao comparar a potência dos testes. Eu obtenho teste invariante ótimo que é também localmente uniformemente mais potente (LUMPI). Construo o envelope de potência para o teste LUMPI e mostro que ele é virtualmente UMP, pois a potência do teste está muito próxima ao envelope (considerando as estruturas espaciais definidas na dissertação). Eu sugiro um procedimento prático para construir um teste que tem boa potência em uma gama de situações onde talvez o teste LUMPI não tenha boas propriedades. Eu concluo que a potência do teste aumenta com o tamanho da amostra e com o parâmetro de dependência espacial (o que está de acordo com a literatura). Entretanto, disputo a visão consensual que a potência do teste diminui a medida que a matriz de vizinhança fica mais densa. Isto reflete um erro de medida comum na literatura, pois a distância estatística entre a hipótese nula e a alternativa varia muito com a estrutura da matriz. Fazendo a correção, concluo que a potência do teste aumenta com a distância da alternativa à nula, como esperado.
This dissertation focus on spatial stochastic process on a lattice (Cliff & Ord--type of models). My contribution consists of using Edgeworth and saddlepoint series to investigate small sample size and power properties of tests for detecting spatial dependence in spatial autoregressive (SAR) stochastic processes, and proposing a new class of spatial econometric models where the spatial dependence parameters that enter the mean structure are different from the ones in the covariance structure. This allows a clearer interpretation of models' parameters and generalizes the set of local and global models suggested by Anselin (2003) as an alternative to the traditional Cliff & Ord models. I propose an estimation procedure for the model's parameters and derive the asymptotic distribution of the parameters' estimators. The suggested model provides some insights on the structure of the commonly used mixed regressive, spatial autoregressive model with spatial autoregressive disturbances (SARAR). The study of the small sample properties of tests to detect spatial dependence expands on the existing literature by allowing the neighborhood structure to be a nonlinear function of the spatial dependence parameter. The use of series approximations instead of the often used Monte Carlo simulation allows a simple way to compare test properties across different neighborhood structures and to correct for size when comparing power. I obtain the power envelope for testing the presence of spatial dependence in the SAR process using the optimal invariant test statistic, which is also locally uniformly most powerful invariant (LUMPI). I have found that the LUMPI test is virtually UMP since its power is very close to the power envelope. I suggest a practical procedure to build a test that, while not UMP, retain good power properties in a wider range for the spatial parameter when compared to the LUMPI test. I find that power increases with sample size and with the spatial dependence parameter -- which agrees with the literature. However, I call into question the consensus view that power decreases as the spatial weight matrix becomes more densely connected. This finding in the literature reflects an error of measure because the hypothesis being compared are at very different statistical distance from the null. After adjusting for this, the power is larger for alternative hypothesis further away from the null -- as one would expect.
31
PENG, SZ-DA, and 彭四達. "On the study and application of modal analysis of structure by vector autoregressive and moving average model." Thesis, 1991. http://ndltd.ncl.edu.tw/handle/54458655903092521907.
Full text
32
Molapo, Mojalefa Aubrey. "Employing Bayesian Vector Auto-Regression (BVAR) method as an altenative technique for forecsating tax revenue in South Africa." Diss., 2017. http://hdl.handle.net/10500/25083.
Full text
33
Yap, Sook Fwe. "Partially nonstationary multivariate autoregressive moving average models." 1992. http://catalog.hathitrust.org/api/volumes/oclc/28744838.html.
Full text
34
"IDENTIFICATION OF PERIODIC AUTOREGRESSIVE MOVING AVERAGE MODELS." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1083682/index.pdf.
Full text
35
LU, SHI-PING, and 盧世屏. "An autoregressive-moving average model for shape analysis." Thesis, 1988. http://ndltd.ncl.edu.tw/handle/58917998612493413928.
Full text
36
Rathmanner, Steven Clifford. "Image texture generation using autoregressive integrated moving average (ARIMA)--models." Thesis, 1987. http://hdl.handle.net/10945/22277.
Full text
37
Chen, Ching-Han, and 陳慶翰. "An Autoregressive Integrated Moving Average Model Based on Genetic Algorithm." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/ummfn2.
Full textAbstract:
碩士國立交通大學
資訊管理研究所
104
Time series forecasting plays an important role in making future decision. The autoregressive integrated moving average (ARIMA) is one of most popular time series forecasting models, but it still can be improved to obtain better accuracy. This thesis proposes an ARIMA model based on genetic algorithm. Compared to the traditional ARIMA model, the proposed model reduces noise among time series through data smoothing, and we hope this design can increase forecasting accuracy. The Nikkei 225 stock index which is used in many previous studies, is selected for experiments. Experimental results indicate that the MAPE of the proposed model is reduced by 34.19% as compared to the MAPE of the ARIMA, and it can verify that the proposed approach achieves better accuracy.
38
Kettner, Marvin. "Persistence exponents via perturbation theory : autoregressive and moving average processes." Phd thesis, 2021. https://tuprints.ulb.tu-darmstadt.de/17566/1/dissertation.pdf.
Full textAbstract:
In this thesis, the persistence problem in the context of Markov chains is studied. We are mainly concerned with processes where the persistence probability converges to zero at exponential speed and we are interested in the rate of decay, the so-called persistence exponent.
For the main results, we use methods from perturbation theory. This approach is completely new in the field of persistence. For this reason, we provide a mostly self-contained presentation of the used theorems of perturbation theory.
We show that the persistence exponent of an autoregressive process of order one can be expressed as a power series in the parameter of the autoregressive process. Additionally, we derive an iterative formula for the coefficients of this power series representation. For moving average processes of order one similar results as in the autoregressive case are derived.
39
Bianchi, Alberto. "Forecasting the automotive market using autoregressive integrated moving average with python." Master's thesis, 2020. http://hdl.handle.net/10362/105993.
Full textAbstract:
The present document is based on an internship at TIPS 4Yand aims to forecast the growth of the automotive market for the next 5 years in Portugal through the Autoregressive Integrated Moving Average model. This report starts with a literature background focused on market development paying particular attention to the electric car market. Hereafter the used methodology is described, from which a detailed explication of the process used for gathering data is defined. Subsequently to this section, a presentation of the results of the tasks is done, followed by a critical opinion about them.
40
Tzeng, Shu-Yang, and 曾恕楊. "A Study of Inventory Management by Using Autoregressive Moving Average Control Chart." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/85688672327056407443.
Full textAbstract:
碩士國立雲林科技大學
工業工程與管理研究所碩士班
94
After Harris proposed the model of Economic Order Quantity(EOQ) in 1915, Many researchers developed new methods of inventory management in order to fit in with real environment. In early 1994, Watts et al. were the pioneers of using the control chart and they applied ITR (Inventory Turnover Rate)、Stockout and demand to monitor the performance of reorder system. Until 1999, Pfohl et al. used the Statistical Process Control ( SPC ) techniques in inventory management system. They used control chart to monitor the inventory level and established decision rules of order quantity according to the change of inventory level. In 2003, 葉卓華indicated that Pfohl et al. did not consider the statistical inspection function of control chart to unusual condition. He used Western rules to rebuild an appropriate control borders in order to detect stockout and demand variation. 王皓翔 used MCEWMA control chart to monitor the demand and he considered not only the demand data is independent but also the demand data is dependent. My research will conduct a new control chart- Autoregressive Moving Average Control Chart (ARMA) to be the main control tool. The ARMA control chart aim not only independent data but also dependent data to be its object. This research will confer the performance of using ARMA control chart to monitor the market demand and compare the difference of inventory systems between above Pfohl et al., 葉卓華, 王皓翔 and me. Keywords：Inventory Management, Statistical Process Control, Autoregressive Moving Average Control Chart
41
Lai, Cheng-Chieh, and 賴正捷. "Applying Time Series Analysis and Autoregressive Moving Average Model in Security Fraud." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/bbbm98.
Full textAbstract:
碩士國立交通大學
科技法律研究所
107
Event study plays an important role in securities fraud cases especially since Halliburton II. One of the core dispute in securities fraud is whether the security’s market price in question had been distorted by the alleged misstatement or omission, while an event study serves as an useful tool in analyzing certain impact on the aforementioned situation. Through the use of market model, event studies predict the security’s would-be price return based on the past market returns, which in turn provides evidence for materiality, loss causation and the calculation of damages. All are essential steps in deciding securities fraud cases. The use of regression analysis in market model is to calculate the price relationships between the security’s price return and market returns. Through these relationships, an event study predicts what the price would have been based on overall market. This method of price prediction eliminates the effect of firm specific misstatement or omission by the use of outside information as its variables. Time series is a sequence of observation taken through time. Data such as a security’s price usually exhibits some form of correlation across time. In this thesis, autoregressive moving average model is discussed to produce a better price prediction based on historical price value, by combining both linear regression and ARMA model, a model can contain movements of both the movement of overall market and historical price value. As a popular common method in model-building, this thesis expect the use of ARMA will be more important for price prediction in future securities fraud cases as a key tool in damage calculation.
42
Su, Yi-Wei, and 蘇意崴. "A Study of Process Capability Indices for ARMA(1,1) Autoregressive Moving Average Models." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/19043810263055548992.
Full textAbstract:
碩士國立雲林科技大學
工業工程與管理系
102
Process capability indices are widely used to indicate the performance and the capability of a manufacturing process. The data were assumed to be normally and independently distributed when using these process capability indices. However, the process data are often autocorrelated in industry and this will cause inaccurate estimation of process capability indices. This study evaluates the effects of the autoregressive parameters ∅1, the moving average parameters θ1 and sample size n in estimation of process mean, process standard deviation, and process capability indices Cp, Cpk, Cpm and Cpmk when the autocorrelated process data is ARMA (1,1). The results show that the process mean will not be affected by the autocorrelation parameters and the sampling size, but the process standard deviation, and the process capability indices Cp, Cpk, Cpm, Cpmk will be affected by the autocorrelation parameters and the sampling size in the ARMA (1,1) model. The process standard deviation will be underestimated and the process capability indices Cp, Cpk, Cpm, and Cpmk will be overestimated if autocorrelation is ignored.
43
"Simultaneous prediction intervals for autoregressive integrated moving average models in the presence of outliers." 2001. http://library.cuhk.edu.hk/record=b5890772.
Full textAbstract:
Cheung Tsai-Yee Crystal.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.
Includes bibliographical references (leaves 83-85).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- The Importance of Forecasting --- p.1
Chapter 2 --- Methodology --- p.5
Chapter 2.1 --- Basic Idea --- p.5
Chapter 2.2 --- Outliers in Time Series --- p.9
Chapter 2.2.1 --- One Outlier Case --- p.9
Chapter 2.2.2 --- Two Outliers Case --- p.17
Chapter 2.2.3 --- General Case --- p.22
Chapter 2.2.4 --- Time Series Parameters are Unknown --- p.24
Chapter 2.3 --- Iterative Procedure for Detecting Outliers --- p.25
Chapter 2.3.1 --- General Procedure for Detecting Outliers --- p.25
Chapter 2.4 --- Methods of Constructing Simultaneous Prediction Intervals --- p.27
Chapter 2.4.1 --- The Bonferroni Method --- p.28
Chapter 2.4.2 --- The Exact Method --- p.28
Chapter 3 --- An Illustrative Example --- p.29
Chapter 3.1 --- Case A --- p.31
Chapter 3.2 --- Case B --- p.32
Chapter 3.3 --- Comparison --- p.33
Chapter 4 --- Simulation Study --- p.36
Chapter 4.1 --- Generate AR(1) with an Outlier --- p.36
Chapter 4.1.1 --- Case A --- p.38
Chapter 4.1.2 --- Case B --- p.40
Chapter 4.2 --- Simulation Results I --- p.42
Chapter 4.3 --- Generate AR(1) with Two Outliers --- p.45
Chapter 4.4 --- Simulation Results II --- p.46
Chapter 4.5 --- Concluding Remarks --- p.47
Bibliography --- p.83
44
Shih-TingHuang and 黃詩婷. "Predicting meteorological droughts in Taiwan using quantile regression and autoregressive integrated moving average models." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/68458564609643377969.
Full textAbstract:
碩士國立成功大學
水利及海洋工程學系
104
Drought is an important issue in global climate change study. In recent years, drought frequency and duration has been increased gradually in Taiwan. Since uneven temporal and spatial distribution of rainfall, stable water supply is heavily fluctuating streamflow. Therefore, establishing drought forecast in order to reduce water-deficit risk is an effective and useful approach in water resources management. The standardized precipitation index (SPI) is used to define meteorological drought in this study. Drought prediction models are constructed by the autoregressive integrated moving average (ARIMA) model and quantile regression model. A total of 4 rainfall gauge stations located in northern, central, southern, and eastern Taiwan are selected in this study. Daily rainfall records of the 1947-2014 period are used to construct SPI series various time-scales (3, 6, and 12 months). The results show that longer time-scale ARIMA and quantile regression models have lower prediction error than the short time-scale models. In terms of mean square error, Taitung station has less errors in both ARIMA and quantile regression models. Quantile regression model generally outperforms the ARIMA model for the longer time-scale predictions.
45
Lee, Fwu Sheng, and 李福生. "APPLICATION OF AUTOREGRESSIVE-INTEGRATED-MOVING AVERAGE TRANSFER FUNCTION MODEL TO CUSTOMER SHORT TERM LOAD FORECASTING." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/75640237149511371677.
Full textAbstract:
碩士國立中山大學
電機工程研究所
83
Short-term load forecast plays an important role in electric power system operation and planning. An accurate load forecast does not only reduce the generation cost in a power system, but also provide a good principle of effective operation. In this thesis, the Box-Jenkins transfer function model is applied to the short term load forecasting by considering weather-load relationship. For four different customer classes in Taipower system, which are residential load, commercial load, institutional load and industrial load, the summer transfer function models have been derived to proceed the short-term load forecast during one week. To demonstrate the effectiveness of the proposed method, this thesis compares the result of the transfer function model with the univariate ARIMA model. Besides the transfer function model's accuracy of the load forecast of weekend and workday is thoroughly investigated. To improve the accuracy level of load forecast, the temperature effect is included in the transfer function. According to the short term load forecasting of different customer classes, it is concluded that the transfer function can achieve better accuracy of load forecast than ARIMA model by consider the causality between power consumption and temperature.
46
吳文誌. "A neural network architecture on order determination and parameter estimation of autoregressive moving average model." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/42251193062520150489.
Full text
47
Leung, Siu Yun. "Feature extraction and reconstruction of two dimensional patterns using autoregressive moving average models and Fourier descriptors." Thesis, 1989. http://spectrum.library.concordia.ca/3259/1/ML51309.pdf.
Full text
48
Riba, Evans Mogolo. "Exploring advanced forecasting methods with applications in aviation." Diss., 2021. http://hdl.handle.net/10500/27410.
Full textAbstract:
Abstracts in English, Afrikaans and Northern SothoMore time series forecasting methods were researched and made available in recent years. This is mainly due to the emergence of machine learning methods which also found applicability in time series forecasting. The emergence of a variety of methods and their variants presents a challenge when choosing appropriate forecasting methods. This study explored the performance of four advanced forecasting methods: autoregressive integrated moving averages (ARIMA); artificial neural networks (ANN); support vector machines (SVM) and regression models with ARIMA errors. To improve their performance, bagging was also applied. The performance of the different methods was illustrated using South African air passenger data collected for planning purposes by the Airports Company South Africa (ACSA). The dissertation discussed the different forecasting methods at length. Characteristics such as strengths and weaknesses and the applicability of the methods were explored. Some of the most popular forecast accuracy measures were discussed in order to understand how they could be used in the performance evaluation of the methods. It was found that the regression model with ARIMA errors outperformed all the other methods, followed by the ARIMA model. These findings are in line with the general findings in the literature. The ANN method is prone to overfitting and this was evident from the results of the training and the test data sets. The bagged models showed mixed results with marginal improvement on some of the methods for some performance measures. It could be concluded that the traditional statistical forecasting methods (ARIMA and the regression model with ARIMA errors) performed better than the machine learning methods (ANN and SVM) on this data set, based on the measures of accuracy used. This calls for more research regarding the applicability of the machine learning methods to time series forecasting which will assist in understanding and improving their performance against the traditional statistical methods
Die afgelope tyd is verskeie tydreeksvooruitskattingsmetodes ondersoek as gevolg van die ontwikkeling van masjienleermetodes met toepassings in die vooruitskatting van tydreekse. Die nuwe metodes en hulle variante laat ʼn groot keuse tussen vooruitskattingsmetodes. Hierdie studie ondersoek die werkverrigting van vier gevorderde vooruitskattingsmetodes: outoregressiewe, geïntegreerde bewegende gemiddeldes (ARIMA), kunsmatige neurale netwerke (ANN), steunvektormasjiene (SVM) en regressiemodelle met ARIMA-foute. Skoenlussaamvoeging is gebruik om die prestasie van die metodes te verbeter. Die prestasie van die vier metodes is vergelyk deur hulle toe te pas op Suid-Afrikaanse lugpassasiersdata wat deur die Suid-Afrikaanse Lughawensmaatskappy (ACSA) vir beplanning ingesamel is. Hierdie verhandeling beskryf die verskillende vooruitskattingsmetodes omvattend. Sowel die positiewe as die negatiewe eienskappe en die toepasbaarheid van die metodes is uitgelig. Bekende prestasiemaatstawwe is ondersoek om die prestasie van die metodes te evalueer. Die regressiemodel met ARIMA-foute en die ARIMA-model het die beste van die vier metodes gevaar. Hierdie bevinding strook met dié in die literatuur. Dat die ANN-metode na oormatige passing neig, is deur die resultate van die opleidings- en toetsdatastelle bevestig. Die skoenlussamevoegingsmodelle het gemengde resultate opgelewer en in sommige prestasiemaatstawwe vir party metodes marginaal verbeter. Op grond van die waardes van die prestasiemaatstawwe wat in hierdie studie gebruik is, kan die gevolgtrekking gemaak word dat die tradisionele statistiese vooruitskattingsmetodes (ARIMA en regressie met ARIMA-foute) op die gekose datastel beter as die masjienleermetodes (ANN en SVM) presteer het. Dit dui op die behoefte aan verdere navorsing oor die toepaslikheid van tydreeksvooruitskatting met masjienleermetodes om hul prestasie vergeleke met dié van die tradisionele metodes te verbeter.
Go nyakišišitšwe ka ga mekgwa ye mentši ya go akanya ka ga molokoloko wa dinako le go dirwa gore e hwetšagale mo mengwageng ye e sa tšwago go feta. Se k e k a le b a k a la g o t šwelela ga mekgwa ya go ithuta ya go diriša metšhene yeo le yona e ilego ya dirišwa ka kakanyong ya molokolokong wa dinako. Go t šwelela ga mehutahuta ya mekgwa le go fapafapana ga yona go tšweletša tlhohlo ge go kgethwa mekgwa ya maleba ya go akanya. Dinyakišišo tše di lekodišišitše go šoma ga mekgwa ye mene ya go akanya yeo e gatetšego pele e lego: ditekanyotshepelo tšeo di kopantšwego tša poelomorago ya maitirišo (ARIMA); dinetweke tša maitirelo tša nyurale (ANN); metšhene ya bekthara ya thekgo (SVM); le mekgwa ya poelomorago yeo e nago le diphošo tša ARIMA. Go kaonafatša go šoma ga yona, nepagalo ya go ithuta ka metšhene le yona e dirišitšwe. Go šoma ga mekgwa ye e fepafapanego go laeditšwe ka go šomiša tshedimošo ya banamedi ba difofane ba Afrika Borwa yeo e kgobokeditšwego mabakeng a dipeakanyo ke Khamphani ya Maemafofane ya Afrika Borwa (ACSA). Sengwalwanyaki šišo se ahlaahlile mekgwa ya kakanyo ye e fapafapanego ka bophara. Dipharologanyi tša go swana le maatla le bofokodi le go dirišega ga mekgwa di ile tša šomišwa. Magato a mangwe ao a tumilego kudu a kakanyo ye e nepagetšego a ile a ahlaahlwa ka nepo ya go kwešiša ka fao a ka šomišwago ka gona ka tshekatshekong ya go šoma ga mekgwa ye. Go hweditšwe gore mokgwa wa poelomorago wa go ba le diphošo tša ARIMA o phadile mekgwa ye mengwe ka moka, gwa latela mokgwa wa ARIMA. Dikutollo tše di sepelelana le dikutollo ka kakaretšo ka dingwaleng. Mo k gwa wa ANN o ka fela o fetišiša gomme se se bonagetše go dipoelo tša tlhahlo le dihlo pha t ša teko ya tshedimošo. Mekgwa ya nepagalo ya go ithuta ka metšhene e bontšhitše dipoelo tšeo di hlakantšwego tšeo di nago le kaonafalo ye kgolo go ye mengwe mekgwa ya go ela go phethagatšwa ga mešomo. Go ka phethwa ka gore mekgwa ya setlwaedi ya go akanya dipalopalo (ARIMA le mokgwa wa poelomorago wa go ba le diphošo tša ARIMA) e šomile bokaone go phala mekgwa ya go ithuta ka metšhene (ANN le SVM) ka mo go sehlopha se sa tshedimošo, go eya ka magato a nepagalo ya magato ao a šomišitšwego. Se se nyaka gore go dirwe dinyakišišo tše dingwe mabapi le go dirišega ga mekgwa ya go ithuta ka metšhene mabapi le go akanya molokoloko wa dinako, e lego seo se tlago thuša go kwešiša le go kaonafatša go šoma ga yona kgahlanong le mekgwa ya setlwaedi ya dipalopalo.
Decision Sciences
M. Sc. (Operations Research)
49
Yueh-Hsia, Lu, and 盧月霞. "Using back propagation network, multivariate adaptive regression splines, and autoregressive integrated moving average to design forecasting models for stock price." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/06346670509726255886.
Full textAbstract:
碩士輔仁大學
管理學研究所
96
Investing in stock market is the most popular and easiest way for investors. Everybody knows trading, but not all can make profit. As Taiwanese stock market doesn’t lie in the scope of efficient markets hypothesis, so investors can use reliable forecasting tools in predicting the trend and variation of stock prices. The purpose of this paper is to investigate the stock market forecasting capability of backpropagation neural network (BPN), multivariate adaptive regression splines (MARS), ARIMA, and hybrid MARS+BPN forecasting techniques. In order to evaluate the performance of the four proposed forecasting models, two public companies listed in Taiwan Stock Market are adopted as illustrative examples. The empirical results indicate that MARS and BPN provide better forecasting results in terms of several performance criteria. Besides, the obtained basis functions of MARS forecasting method can provide useful information for better investment decisions.
50
Dai, Jun-Ting, and 戴郡婷. "Applying Autoregressive Integrated Moving Average Model and Computational Intelligence Approaches to the Predictions of Prices for Rice, Corn, Wheat and Soybean." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/88379481560627361265.
Full textAbstract:
碩士輔仁大學
統計資訊學系應用統計碩士在職專班
104
In recent years the supplies of major food crops worldwide (i.e., rice, corn, wheat, and soybean) have gradually decreased because of global warming, a lack of arable land, and rapid population growth. In addition, the rapid increase in demand for food has contributed toward the continuous rise of food prices, directly threatening the lives of over 800 million people around the world who are reported to be chronically undernourished. Therefore, food shortages have become a crucial concern worldwide. Because the United States is one of the four major exporters of food crops, this study collected the prices of major food crops as reported by the U.S. Department of Agriculture from January 1990 to September 2015. This study applied an autoregressive integrated moving average (ARIMA) model; an artificial neural network (ANN); support vector regression (SVR); multivariate adaptive regression spline (MARS); and a hybrid approach to construct models for predicting the prices of the four major food crops. The predictive effectiveness of these models was compared. The results showed that the best prediction model for rice,corn,wheat and soybeans were ARIMA–SVR, ARIMA–MARS, ARIMA–ANN and ARIMA–MARS, respectirely.