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Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Model time series analysis"

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Zhuravka, Fedir, Hanna Filatova, Petr Šuleř, and Tomasz Wołowiec. "State debt assessment and forecasting: time series analysis." Investment Management and Financial Innovations 18, no. 1 (January 28, 2021): 65–75. http://dx.doi.org/10.21511/imfi.18(1).2021.06.

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One of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from December 2004 to November 2020. A visual analysis of the dynamics of state debt led to the conclusion about the unstable debt situation in Ukraine and a significant increase in debt over the past six years. Using the Hurst exponent, the paper provides the calculated value of the level of persistence in time series data. Based on the obtained indicator, a conclusion was made on the confirmation of expediency to use autoregressive models for predicting future dynamics of Ukraine’s state debt. Using the EViews software, the procedure for forecasting Ukraine’s state debt by utilizing the ARIMA model was illustrated, i.e., the series was tested for stationarity, the time series of monthly state debt data were converted to stationary, the model parameters were determined and, as a result, the most optimal specification of the ARIMA model was selected.
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2

Huang, Guangdong, and Jiahong Li. "Hybrid Time Series Method for Long-Time Temperature Series Analysis." Discrete Dynamics in Nature and Society 2021 (July 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/9968022.

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This paper combines discrete wavelet transform (DWT), autoregressive moving average (ARMA), and XGBoost algorithm to propose a weighted hybrid algorithm named DWTs-ARMA-XGBoost (DAX) on long-time temperature series analysis. Firstly, this paper chooses the temperature data of February 1 to 20 from 1967 to 2016 of northern mountainous area in North China as the observed data. Then, we use 10 different discrete wavelet functions to decompose and reconstruct the observed data. Next, we build ARMA models on all the reconstructed data. In the end, we regard the calculations of 10 DWT-ARMA (DA) algorithms and the observed data as the labels and target of the XGBoost algorithm, respectively. Through the data training and testing of the XGBoost algorithm, the optimal weights and the corresponding output of the hybrid DAX model can be calculated. Root mean squared error (RMSE) was followed as the criteria for judging the precision. This paper compared DAX with an equal-weighted average (EWA) algorithm and 10 DA algorithms. The result shows that the RMSE of the two hybrid algorithms is much lower than that of the DA algorithms. Moreover, the bigger decrease in RMSE of the DAX model than the EWA model represents that the proposed DAX model has significant superiority in combining models which proves that DAX has significant improvement in prediction as well.
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3

Anupriya and Anita Singhrova. "Comparative Analysis of Time Series Forecasting Models for SDMN Traffic." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 531–40. http://dx.doi.org/10.5373/jardcs/v11/20192602.

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4

Bratčikovienė, Nomeda. "Adapted SETAR model for lithuanian HCPI time series." Nonlinear Analysis: Modelling and Control 17, no. 1 (January 25, 2012): 27–46. http://dx.doi.org/10.15388/na.17.1.14076.

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Анотація:
We present adapted SETAR (self-exciting threshold autoregressive) model, which enables simultaneous estimation of nonlinearity and unobserved time series components. This model was tested on real Lithuanian harmonised consumer price index (HCPI) time series, covering the period from January 1996 to December 2009. The results show that adapted SETAR model is able to capture features of the real time series with complex nature. ARIMA model has also been used for the same time series for the comparison. Evaluated models and results of the comparison are presented in this work.
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5

Momani, P. E. Naill M. "Time Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis." American Journal of Environmental Sciences 5, no. 5 (May 1, 2009): 599–604. http://dx.doi.org/10.3844/ajessp.2009.599.604.

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6

TSAUR, RUEY-CHYN, HSIAO-FAN WANG, and JIA-CHI O.-YANG. "FUZZY REGRESSION FOR SEASONAL TIME SERIES ANALYSIS." International Journal of Information Technology & Decision Making 01, no. 01 (March 2002): 165–75. http://dx.doi.org/10.1142/s0219622002000117.

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Fuzzy regression model is an alternative to evaluate the relation between independent variables and dependent variable among the forecasting models when the data are not sufficient to identify the relation. Such phenomenon is significant especially for seasonal variation data for which large amount of data are required to show the pattern. However, few researches have been done on this issue. Because of its increasing importance in industries, in this study, we propose a method of applying fuzzy regression model for this purpose. By using two independent variables of preceding periodical data and index of time, the developed model not only shows the pattern of the seasonal variation, but also the yearly trend. From the results of the illustration, the average forecasting error is below 1.85% which, in comparison to the most commonly used Quadratic Trend Analysis of 2.91% and the Double Exponential Smoothing Model of 4.29%, has a better performance.
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Kim, Hyesuk, and Incheol Kim. "Human Activity Recognition as Time-Series Analysis." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/676090.

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We propose a system that can recognize daily human activities with a Kinect-style depth camera. Our system utilizes a set of view-invariant features and the hidden state conditional random field (HCRF) model to recognize human activities from the 3D body pose stream provided by MS Kinect API or OpenNI. Many high-level daily activities can be regarded as having a hierarchical structure where multiple subactivities are performed sequentially or iteratively. In order to model effectively these high-level daily activities, we utilized a multiclass HCRF model, which is a kind of probabilistic graphical models. In addition, in order to get view-invariant, but more informative features, we extract joint angles from the subject’s skeleton model and then perform the feature transformation to obtain three different types of features regarding motion, structure, and hand positions. Through various experiments using two different datasets, KAD-30 and CAD-60, the high performance of our system is verified.
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Novotny, V., H. Jones, X. Feng, and A. Capodaglio. "Time Series Analysis Models of Activated Sludge Plants." Water Science and Technology 23, no. 4-6 (February 1, 1991): 1107–16. http://dx.doi.org/10.2166/wst.1991.0562.

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Time series models of the activated sludge process are very useful in design and real time operation of wastewater treatment systems which deal with variable influent flows and pollution loads. In contrast to common deterministic dynamic mathematical models which require knowledge of a large number of coefficients, the time series models can be developed from input and output monitoring data series. In order to avoid “black box” approaches, time series models can be made compatible and identical in principle, with their dynamic mass balance model equivalents. In fact, these two types of models may differ only in nomenclature. ARMA-Transfer Function models can be used for systems which are linear or can be linearized such as typical BOD or suspended solids influent-effluent relationships for which the type of model is known. For systems which are highly nonlinear, and/or the input-output model is unknown, neural network models can be used. Both ARMA-TF models and neural network models can be made self-learning, that is, the performance of the model can be periodically improved manually or in an automated mode as new information is collected by monitoring. Application examples are included.
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Ince, Huseyin, and Fatma Sonmez Cakir. "Analysis of financial time series with model hybridization." Pressacademia 4, no. 3 (September 30, 2017): 331–41. http://dx.doi.org/10.17261/pressacademia.2017.700.

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Parzen, E. "Time Series Model Identification and Quantile Spectral Analysis." IFAC Proceedings Volumes 18, no. 5 (July 1985): 731–36. http://dx.doi.org/10.1016/s1474-6670(17)60647-5.

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Дисертації з теми "Model time series analysis"

1

Billah, Baki 1965. "Model selection for time series forecasting models." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/8840.

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Pope, Kenneth James. "Time series analysis." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318445.

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Alexander, Miranda Abhilash. "Spectral factor model for time series learning." Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209812.

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Анотація:
Today's computerized processes generate

massive amounts of streaming data.

In many applications, data is collected for modeling the processes. The process model is hoped to drive objectives such as decision support, data visualization, business intelligence, automation and control, pattern recognition and classification, etc. However, we face significant challenges in data-driven modeling of processes. Apart from the errors, outliers and noise in the data measurements, the main challenge is due to a large dimensionality, which is the number of variables each data sample measures. The samples often form a long temporal sequence called a multivariate time series where any one sample is influenced by the others.

We wish to build a model that will ensure robust generation, reviewing, and representation of new multivariate time series that are consistent with the underlying process.

In this thesis, we adopt a modeling framework to extract characteristics from multivariate time series that correspond to dynamic variation-covariation common to the measured variables across all the samples. Those characteristics of a multivariate time series are named its 'commonalities' and a suitable measure for them is defined. What makes the multivariate time series model versatile is the assumption regarding the existence of a latent time series of known or presumed characteristics and much lower dimensionality than the measured time series; the result is the well-known 'dynamic factor model'.

Original variants of existing methods for estimating the dynamic factor model are developed: The estimation is performed using the frequency-domain equivalent of the dynamic factor model named the 'spectral factor model'. To estimate the spectral factor model, ideas are sought from the asymptotic theory of spectral estimates. This theory is used to attain a probabilistic formulation, which provides maximum likelihood estimates for the spectral factor model parameters. Then, maximum likelihood parameters are developed with all the analysis entirely in the spectral-domain such that the dynamically transformed latent time series inherits the commonalities maximally.

The main contribution of this thesis is a learning framework using the spectral factor model. We term learning as the ability of a computational model of a process to robustly characterize the data the process generates for purposes of pattern matching, classification and prediction. Hence, the spectral factor model could be claimed to have learned a multivariate time series if the latent time series when dynamically transformed extracts the commonalities reliably and maximally. The spectral factor model will be used for mainly two multivariate time series learning applications: First, real-world streaming datasets obtained from various processes are to be classified; in this exercise, human brain magnetoencephalography signals obtained during various cognitive and physical tasks are classified. Second, the commonalities are put to test by asking for reliable prediction of a multivariate time series given its past evolution; share prices in a portfolio are forecasted as part of this challenge.

For both spectral factor modeling and learning, an analytical solution as well as an iterative solution are developed. While the analytical solution is based on low-rank approximation of the spectral density function, the iterative solution is based on the expectation-maximization algorithm. For the human brain signal classification exercise, a strategy for comparing similarities between the commonalities for various classes of multivariate time series processes is developed. For the share price prediction problem, a vector autoregressive model whose parameters are enriched with the maximum likelihood commonalities is designed. In both these learning problems, the spectral factor model gives commendable performance with respect to competing approaches.

Les processus informatisés actuels génèrent des quantités massives de flux de données. Dans nombre d'applications, ces flux de données sont collectées en vue de modéliser les processus. Les modèles de processus obtenus ont pour but la réalisation d'objectifs tels que l'aide à la décision, la visualisation de données, l'informatique décisionnelle, l'automatisation et le contrôle, la reconnaissance de formes et la classification, etc. La modélisation de processus sur la base de données implique cependant de faire face à d’importants défis. Outre les erreurs, les données aberrantes et le bruit, le principal défi provient de la large dimensionnalité, i.e. du nombre de variables dans chaque échantillon de données mesurées. Les échantillons forment souvent une longue séquence temporelle appelée série temporelle multivariée, où chaque échantillon est influencé par les autres. Notre objectif est de construire un modèle robuste qui garantisse la génération, la révision et la représentation de nouvelles séries temporelles multivariées cohérentes avec le processus sous-jacent.

Dans cette thèse, nous adoptons un cadre de modélisation capable d’extraire, à partir de séries temporelles multivariées, des caractéristiques correspondant à des variations - covariations dynamiques communes aux variables mesurées dans tous les échantillons. Ces caractéristiques sont appelées «points communs» et une mesure qui leur est appropriée est définie. Ce qui rend le modèle de séries temporelles multivariées polyvalent est l'hypothèse relative à l'existence de séries temporelles latentes de caractéristiques connues ou présumées et de dimensionnalité beaucoup plus faible que les séries temporelles mesurées; le résultat est le bien connu «modèle factoriel dynamique». Des variantes originales de méthodes existantes pour estimer le modèle factoriel dynamique sont développées :l'estimation est réalisée en utilisant l'équivalent du modèle factoriel dynamique au niveau du domaine de fréquence, désigné comme le «modèle factoriel spectral». Pour estimer le modèle factoriel spectral, nous nous basons sur des idées relatives à la théorie des estimations spectrales. Cette théorie est utilisée pour aboutir à une formulation probabiliste, qui fournit des estimations de probabilité maximale pour les paramètres du modèle factoriel spectral. Des paramètres de probabilité maximale sont alors développés, en plaçant notre analyse entièrement dans le domaine spectral, de façon à ce que les séries temporelles latentes transformées dynamiquement héritent au maximum des points communs.

La principale contribution de cette thèse consiste en un cadre d'apprentissage utilisant le modèle factoriel spectral. Nous désignons par apprentissage la capacité d'un modèle de processus à caractériser de façon robuste les données générées par le processus à des fins de filtrage par motif, classification et prédiction. Dans ce contexte, le modèle factoriel spectral est considéré comme ayant appris une série temporelle multivariée si la série temporelle latente, une fois dynamiquement transformée, permet d'extraire les points communs de façon fiable et maximale. Le modèle factoriel spectral sera utilisé principalement pour deux applications d'apprentissage de séries multivariées :en premier lieu, des ensembles de données sous forme de flux venant de différents processus du monde réel doivent être classifiés; lors de cet exercice, la classification porte sur des signaux magnétoencéphalographiques obtenus chez l'homme au cours de différentes tâches physiques et cognitives; en second lieu, les points communs obtenus sont testés en demandant une prédiction fiable d'une série temporelle multivariée étant donnée l'évolution passée; les prix d'un portefeuille d'actions sont prédits dans le cadre de ce défi.

À la fois pour la modélisation et pour l'apprentissage factoriel spectral, une solution analytique aussi bien qu'une solution itérative sont développées. Tandis que la solution analytique est basée sur une approximation de rang inférieur de la fonction de densité spectrale, la solution itérative est basée, quant à elle, sur l'algorithme de maximisation des attentes. Pour l'exercice de classification des signaux magnétoencéphalographiques humains, une stratégie de comparaison des similitudes entre les points communs des différentes classes de processus de séries temporelles multivariées est développée. Pour le problème de prédiction des prix des actions, un modèle vectoriel autorégressif dont les paramètres sont enrichis avec les points communs de probabilité maximale est conçu. Dans ces deux problèmes d’apprentissage, le modèle factoriel spectral atteint des performances louables en regard d’approches concurrentes.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

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Yin, Jiang Ling. "Financial time series analysis." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2492929.

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Assefa, Yared. "Time series and spatial analysis of crop yield." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/15142.

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Анотація:
Master of Science
Department of Statistics
Juan Du
Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have a space and time dependence. This work focuses on the modeling and analysis of crop yield over space and time. Specifically, two different yield data sets were used. The first yield and environmental data set was collected across selected counties in Kansas from yield performance tests conducted for multiple years. The second yield data set was a survey data set collected by USDA across the US from 1900-2009. The objectives of our study were to investigate crop yield trends in space and time, quantify the variability in yield explained by genetics and space-time (environment) factors, and study how spatio-temporal information could be incorporated and also utilized in modeling and forecasting yield. Based on the format of these data sets, trend of irrigated and dryland crops was analyzed by employing time series statistical techniques. Some traditional linear regressions and smoothing techniques are first used to obtain the yield function. These models were then improved by incorporating time and space information either as explanatory variables or as auto- or cross- correlations adjusted in the residual covariance structures. In addition, a multivariate time series modeling approach was conducted to demonstrate how the space and time correlation information can be utilized to model and forecast yield and related variables. The conclusion from this research clearly emphasizes the importance of space and time components of data sets in research analysis. That is partly because they can often adjust (make up) for those underlying variables and factor effects that are not measured or not well understood.
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Wong, Wing-mei. "Some topics in model selection in financial time series analysis." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23273112.

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Li, Chun-wah. "On a double threshold autoregressive heteroskedastic time series model /." [Hong Kong : University of Hong Kong], 1994. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13745037.

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8

Guthrey, Delparde Raleigh. "Time series analysis of ozone data." CSUSB ScholarWorks, 1998. https://scholarworks.lib.csusb.edu/etd-project/1788.

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Hossain, Shahadat. "Complete Bayesian analysis of some mixture time series models." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/complete-bayesian-analysis-of-some-mixture-time-series-models(6746d653-e08f-4866-ace9-29586f8160f6).html.

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In this thesis we consider some finite mixture time series models in which each component is following a well-known process, e.g. AR, ARMA or ARMA-GARCH process, with either normal-type errors or Student-t type errors. We develop MCMC methods and use them in the Bayesian analysis of these mixture models. We introduce some new models such as mixture of Student-t ARMA components and mixture of Student-t ARMA-GARCH components with complete Bayesian treatments. Moreover, we use component precision (instead of variance) with an additional hierarchical level which makes our model more consistent with the MCMC moves. We have implemented the proposed methods in R and give examples with real and simulated data.
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Lee, Yee-nin, and 李綺年. "On a double smooth transition time series model." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31215555.

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Книги з теми "Model time series analysis"

1

Chih-Ling, Tsai, ed. Regression and time series model selection. Singapore: World Scientific, 1998.

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2

Frances, Philip Hans. Model selection and seasonality in time series. Amsterdam: Tinbergen Instituut, 1991.

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3

Konstantinos, Fokianos, ed. Regression models for time series analysis. Hoboken, N.J: Wiley-Interscience, 2002.

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4

Harvey, A. C. Time series models. 2nd ed. New York: Harvester Wheatsheaf, 1992.

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5

Time series models. 2nd ed. Cambridge, Mass: MIT Press, 1993.

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6

Harvey, A. C. Time series models. 2nd ed. New York: Harvester Wheatsheaf, 1993.

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7

Franses, Philip Hans. Model selection and seasonality in time series. Amsterdam: Thesis/Tinbergen Instituut, 1991.

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8

1958-, Williams John T., ed. Multiple time series models. Thousand Oaks, Calif: Sage Publications, 2007.

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9

Durlauf, Steven N., and Lawrence Blume. Macroeconometrics and time series analysis. Basingstoke: Palgrave Macmillan, 2010.

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Franses, Philip Hans. Periodic time series models. Oxford: Oxford University Press, 2004.

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Частини книг з теми "Model time series analysis"

1

Kotsifakos, Alexios, and Panagiotis Papapetrou. "Model-Based Time Series Classification." In Advances in Intelligent Data Analysis XIII, 179–91. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12571-8_16.

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2

Lee, Cheng-Few, John C. Lee, and Alice C. Lee. "Time Series: Analysis, Model, and Forecasting." In Statistics for Business and Financial Economics, 927–72. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5897-5_18.

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3

Lee, Cheng-Few, Hong-Yi Chen, and John Lee. "Time Series: Analysis, Model, and Forecasting." In Financial Econometrics, Mathematics and Statistics, 279–316. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9429-8_10.

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4

Lee, Cheng-Few, John Lee, Jow-Ran Chang, and Tzu Tai. "Time Series: Analysis, Model, and Forecasting." In Essentials of Excel, Excel VBA, SAS and Minitab for Statistical and Financial Analyses, 589–644. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38867-0_18.

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5

Moy, Ronald L., Li-Shya Chen, and Lie Jane Kao. "Time-Series: Analysis, Model, and Forecasting." In Study Guide for Statistics for Business and Financial Economics, 283–307. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11997-7_18.

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6

Alonso, Fernando, Loïc Martínez, César Montes, Aurora Pérez, Agustín Santamaría, and Juan Pedro Valente. "Semantic Reference Model in Medical Time Series." In Biological and Medical Data Analysis, 344–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30547-7_35.

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7

Hagiwara, Junichiro. "Quick Tour of Time Series Analysis." In Time Series Analysis for the State-Space Model with R/Stan, 29–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0711-0_4.

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8

Segev, Arie, and Rakesh Chandra. "A data model for time-series analysis." In Lecture Notes in Computer Science, 191–212. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57507-3_10.

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Ruppert, David, and David S. Matteson. "Time Series Models: Basics." In Statistics and Data Analysis for Financial Engineering, 307–60. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2614-5_12.

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Ruppert, David. "Time Series Models: Basics." In Statistics and Data Analysis for Financial Engineering, 201–55. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7787-8_9.

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Тези доповідей конференцій з теми "Model time series analysis"

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Zhou, Yu. "Failure trend analysis using time series model." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7978640.

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Herriot, G., B. L. Ellerbroek, D. A. Andersen, M. Schoeck, and T. Travouillon. "An Auto-Regressive Model to Create Seeing Time Series." In Adaptive Optics: Methods, Analysis and Applications. Washington, D.C.: OSA, 2009. http://dx.doi.org/10.1364/aopt.2009.aothb1.

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Bin-Sheng Liu and Qi-Shu Pan. "A combination forecasting model to chaotic time series." In 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4420779.

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Chen, Huanhuan, Fengzhen Tang, Peter Tino, and Xin Yao. "Model-based kernel for efficient time series analysis." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2487575.2487700.

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Agarwal, Anish, Muhammad Jehangir Amjad, Devavrat Shah, and Dennis Shen. "Model Agnostic Time Series Analysis via Matrix Estimation." In SIGMETRICS '19: ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3309697.3331479.

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Xu, Wen-Bin, Yong Qi, and Di Hou. "Multi-dimensional time series-based application server aging model." In 2010 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2010. http://dx.doi.org/10.1109/icwapr.2010.5576346.

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Jin, Xue-Bo, Nian-Xiang Yang, Ting-Li Su, and Jian-Lei Kong. "Time-Series Main Trend Analysis by Adaptive Dynamics Model." In 2018 10th International Conference on Modelling, Identification and Control (ICMIC). IEEE, 2018. http://dx.doi.org/10.1109/icmic.2018.8529910.

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Abdulkadir, Said Jadid, and Suet-Peng Yong. "Lorenz time-series analysis using a scaled hybrid model." In 2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC). IEEE, 2015. http://dx.doi.org/10.1109/ismsc.2015.7594082.

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Schlegel, Udo, and Daniel A. Keim. "Time Series Model Attribution Visualizations as Explanations." In 2021 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX). IEEE, 2021. http://dx.doi.org/10.1109/trex53765.2021.00010.

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Freudenthaler, Christoph, Steffen Rendle, Lars Schmidt-Thieme, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Factorizing Markov Models for Categorical Time Series Prediction." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3636749.

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Звіти організацій з теми "Model time series analysis"

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Czaplewski, Raymond L., and Mike T. Thompson. Model-based time-series analysis of FIA panel data absent re-measurements. Ft. Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2013. http://dx.doi.org/10.2737/rmrs-rp-102.

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He, Jiachuan, Scott Hansen, and Velimir Valentinov Vesselinov. Analysis of Hydrologic Time Series Reconstruction UncertaintyDue to Inverse Model InadequacyUsing the Laguerre Expansion Method. Office of Scientific and Technical Information (OSTI), January 2017. http://dx.doi.org/10.2172/1338783.

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Eichenbaum, Martin, Lars Peter Hansen, and Kenneth Singleton. A Time Series Analysis of Representative Agent Models of Consumption andLeisure Choice Under Uncertainty. Cambridge, MA: National Bureau of Economic Research, July 1986. http://dx.doi.org/10.3386/w1981.

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Mohanty, Subhasish, and Joseph Listwan. Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1822853.

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Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada202273.

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Anderson, Theodore W. Time Series Analysis and Multivariate Statistical Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada161375.

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Li, Degui, Oliver Linton, and Zudi Lu. A flexible semiparametric model for time series. Institute for Fiscal Studies, September 2012. http://dx.doi.org/10.1920/wp.cem.2012.2812.

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Michalski, A,, D. Andersson, R. Rossi, and C. Soriano. D7.1 DELIVERY OF GEOMETRY AND COMPUTATIONAL MODEL. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.020.

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Анотація:
This document describes the industrial application, on which the developments of the project are implemented, and the CFD set-up. The developments are implemented over six analysis cases with increasing complexity starting from a 2D geometry with mean wind inflow to a 3D geometry with turbulent inflow and real-time shape optimization. The application represents the CAARC tall building model, which has served as a benchmark model for many studies since the 1970’s when it was first developed. Base moments (bending and torsional moments) of the building are extracted for validation by comparison of the results with the benchmark study. Page 3 of 19 Deliverable 7.1
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Lai, Eric, Daniel Moyer, Baichuan Yuan, Eric Fox, Blake Hunter, Andrea L. Bertozzi, and Jeffrey Brantingham. Topic Time Series Analysis of Microblogs. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada610278.

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Friedman, Avner, Jr Miller, and Willard. Radar/Sonar and Time Series Analysis. Fort Belvoir, VA: Defense Technical Information Center, April 1991. http://dx.doi.org/10.21236/ada238496.

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