Relevant bibliographies by topics / Autoregressive duration model (ACD)

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Academic literature on the topic 'Autoregressive duration model (ACD)'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Autoregressive duration model (ACD)"

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JEYASREEDHARAN, NAGARATNAM, DAVID E. ALLEN, and JOEY WENLING YANG. "YET ANOTHER ACD MODEL: THE AUTOREGRESSIVE CONDITIONAL DIRECTIONAL DURATION (ACDD) MODEL." Annals of Financial Economics 09, no. 01 (2014): 1450004. http://dx.doi.org/10.1142/s2010495214500043.

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This paper features a new autoregressive conditional duration (ACD) model which sits within the theoretical framework provided by the recently developed observation-driven time series models by Creal et al. (2013): the generalized autoregressive score (GAS) models. The autoregressive conditional directional duration (ACDD) model itself contains three novelties. First, durations (intra-trade intervals or waiting-times) are signed, based on whether a (positive) ask-driven trade or a (negative) bid-driven trade occurred. These signed trade-durations are known as directional durations. Second, as the resultant directional durations are no longer positive and asymmetrical but are symmetrically distributed, the familiar generalized autoregressive conditional heteroskedasticity (GARCH)-like formulation of the ACD process is proposed for modeling these directional durations. Consequently, the proposed model is called the ACDD model. Third, using the alternative GARCH-like formulation, persistence or long-memory in the durations is easily addressed both via the mean and variance equations: the mean equation uses a semi-parametric fractional autoregressive (SEMIFAR) formulation and the variance equation uses a GARCH formulation. The paper demonstrates the flexibility and convenience of the generalized autoregressive score (GAS) model framework in the context of a particular ACD model specification. The model can be viewed as an alternative extension of the "asymmetric ACD model" of Bauwens and Giot (2013) which captures information related to the evolution of prices as well as the quote-durations.
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Cunha, Danúbia R., Roberto Vila, Helton Saulo, and Rodrigo N. Fernandez. "A General Family of Autoregressive Conditional Duration Models Applied to High-Frequency Financial Data." Journal of Risk and Financial Management 13, no. 3 (2020): 45. http://dx.doi.org/10.3390/jrfm13030045.

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In this paper, we propose a general family of Birnbaum–Saunders autoregressive conditional duration (BS-ACD) models based on generalized Birnbaum–Saunders (GBS) distributions, denoted by GBS-ACD. We further generalize these GBS-ACD models by using a Box-Cox transformation with a shape parameter λ to the conditional median dynamics and an asymmetric response to shocks; this is denoted by GBS-AACD. We then carry out a Monte Carlo simulation study to evaluate the performance of the GBS-ACD models. Finally, an illustration of the proposed models is made by using New York stock exchange (NYSE) transaction data.
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Vlahogianni, Eleni I., Matthew G. Karlaftis, and Konstantinos Kepaptsoglou. "Nonlinear Autoregressive Conditional Duration Models for Traffic Congestion Estimation." Journal of Probability and Statistics 2011 (2011): 1–13. http://dx.doi.org/10.1155/2011/798953.

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The considerable impact of congestion on transportation networks is reflected by the vast amount of research papers dedicated to congestion identification, modeling, and alleviation. Despite this, the statistical characteristics of congestion, and particularly of its duration, have not been systematically studied, regardless of the fact that they can offer significant insights on its formation, effects and alleviation. We extend previous research by proposing the autoregressive conditional duration (ACD) approach for modeling congestion duration in urban signalized arterials. Results based on data from a signalized arterial indicate that a multiregime nonlinear ACD model best describes the observed congestion duration data while when it lasts longer than 18 minutes, traffic exhibits persistence and slow recovery rate.
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SIN, CHOR-YIU. "QMLE OF A STANDARD EXPONENTIAL ACD MODEL: ASYMPTOTIC DISTRIBUTION AND RESIDUAL CORRELATION." Annals of Financial Economics 09, no. 02 (2014): 1440009. http://dx.doi.org/10.1142/s2010495214400090.

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Since the seminal work by Engle and Russell, (1998), numerous studies have applied their standard/linear ACD(m,q) model (autoregressive conditional duration model of orders m and q) to fit the irregular spaced transaction data. Recently, Araichi et al. (2013) also applied the ACD model to claims in insurance. Many of these papers assume that the standardized error follows a standard exponential distribution. In this paper, we derive the asymptotic distribution of the quasi-maximum likelihood estimator (QMLE) when a standard exponential distribution is used. In other words, we provide robust standard errors for an ACD model. Applying this asymptotic theory, we then derive the asymptotic distribution of the corresponding residual autocorrelation.
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Deo, Rohit, Clifford M. Hurvich, Philippe Soulier, and Yi Wang. "CONDITIONS FOR THE PROPAGATION OF MEMORY PARAMETER FROM DURATIONS TO COUNTS AND REALIZED VOLATILITY." Econometric Theory 25, no. 3 (2009): 764–92. http://dx.doi.org/10.1017/s0266466608090294.

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We establish sufficient conditions on durations that are stationary with finite variance and memory parameter$d \in [0,{\textstyle{1 \over 2}})$to ensure that the corresponding counting processN(t) satisfies VarN(t) ~Ct2d+1(C> 0) ast→ ∞, with the same memory parameter$d \in [0,{\textstyle{1 \over 2}})$that was assumed for the durations. Thus, these conditions ensure that the memory parameter in durations propagates to the same memory parameter in the counts. We then show that any autoregressive conditional duration ACD(1,1) model with a sufficient number of finite moments yields short memory in counts, whereas any long memory stochastic duration model withd> 0 and all finite moments yields long memory in counts, with the samed. Finally, we provide some results about the propagation of long memory to the empirically relevant case of realized variance estimates affected by market microstructure noise contamination.
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Shi, Yong, Wei Dai, Wen Long, and Bo Li. "Improved ACD-Based Financial Trade Durations Prediction Leveraging LSTM Networks and Attention Mechanism." Mathematical Problems in Engineering 2021 (January 29, 2021): 1–11. http://dx.doi.org/10.1155/2021/7854512.

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The liquidity risk factor of security market plays an important role in the formulation of trading strategies. A more liquid stock market means that the securities can be bought or sold more easily. As a sound indicator of market liquidity, the transaction duration is the focus of this study. We concentrate on estimating the probability density function p Δ t i + 1 | G i , where Δ t i + 1 represents the duration of the (i + 1)-th transaction and G i represents the historical information at the time when the (i + 1)-th transaction occurs. In this paper, we propose a new ultrahigh-frequency (UHF) duration modelling framework by utilizing long short-term memory (LSTM) networks to extend the conditional mean equation of classic autoregressive conditional duration (ACD) model while retaining the probabilistic inference ability. And then, the attention mechanism is leveraged to unveil the internal mechanism of the constructed model. In order to minimize the impact of manual parameter tuning, we adopt fixed hyperparameters during the training process. The experiments applied to a large-scale dataset prove the superiority of the proposed hybrid models. In the input sequence, the temporal positions which are more important for predicting the next duration can be efficiently highlighted via the added attention mechanism layer.
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Bortoluzzo, Adriana B., Pedro A. Morettin, and Clelia M. C. Toloi. "Time-varying autoregressive conditional duration model." Journal of Applied Statistics 37, no. 5 (2010): 847–64. http://dx.doi.org/10.1080/02664760902914458.

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Meitz, Mika, and Pentti Saikkonen. "ERGODICITY, MIXING, AND EXISTENCE OF MOMENTS OF A CLASS OF MARKOV MODELS WITH APPLICATIONS TO GARCH AND ACD MODELS." Econometric Theory 24, no. 5 (2008): 1291–320. http://dx.doi.org/10.1017/s0266466608080511.

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This paper studies a class of Markov models that consist of two components. Typically, one of the components is observable and the other is unobservable or “hidden.” Conditions under which geometric ergodicity of the unobservable component is inherited by the joint process formed of the two components are given. This implies existence of initial values such that the joint process is strictly stationary and β-mixing. In addition to this, conditions for the existence of moments are also obtained, and extensions to the case of nonstationary initial values are provided. All these results are applied to a general model that includes as special cases various first-order generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional duration (ACD) models with possibly complicated nonlinear structures. The results only require mild moment assumptions and in some cases provide necessary and sufficient conditions for geometric ergodicity.
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Liang, Y., A. Thavaneswaran, and B. Abraham. "Joint Estimation Using Quadratic Estimating Function." Journal of Probability and Statistics 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/372512.

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A class of martingale estimating functions is convenient and plays an important role for inference for nonlinear time series models. However, when the information about the first four conditional moments of the observed process becomes available, the quadratic estimating functions are more informative. In this paper, a general framework for joint estimation of conditional mean and variance parameters in time series models using quadratic estimating functions is developed. Superiority of the approach is demonstrated by comparing the information associated with the optimal quadratic estimating function with the information associated with other estimating functions. The method is used to study the optimal quadratic estimating functions of the parameters of autoregressive conditional duration (ACD) models, random coefficient autoregressive (RCA) models, doubly stochastic models and regression models with ARCH errors. Closed-form expressions for the information gain are also discussed in some detail.
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Li, W. K., and Philip L. H. Yu. "On the residual autocorrelation of the autoregressive conditional duration model." Economics Letters 79, no. 2 (2003): 169–75. http://dx.doi.org/10.1016/s0165-1765(02)00303-8.

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Dissertations / Theses on the topic "Autoregressive duration model (ACD)"

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Silva, Marília Gabriela Elias da. "Análise das cotações e transações intradiárias da Petrobrás utilizando dados irregularmente espaçados." reponame:Repositório Institucional do FGV, 2014. http://hdl.handle.net/10438/12034.

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Submitted by Marília Gabriela Elias da Silva (marilia.gabriela.es@gmail.com) on 2014-09-18T19:07:04Z No. of bitstreams: 1 Marilia_Gabriela_tese.pdf: 512980 bytes, checksum: 8ab7fc0b5b89fa1bd8f99a705ae51920 (MD5)<br>Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2014-09-18T19:43:45Z (GMT) No. of bitstreams: 1 Marilia_Gabriela_tese.pdf: 512980 bytes, checksum: 8ab7fc0b5b89fa1bd8f99a705ae51920 (MD5)<br>Made available in DSpace on 2014-09-18T19:51:23Z (GMT). No. of bitstreams: 1 Marilia_Gabriela_tese.pdf: 512980 bytes, checksum: 8ab7fc0b5b89fa1bd8f99a705ae51920 (MD5) Previous issue date: 2014-08-27<br>This study uses data provided by BM&FBovespa to analyze Petrobras' stock for the months between July and August 2010 and October 2008. First, we present a detailed discussion about handling data, we show the impossibility of using the mid-price quote due to the high number of buy / sell orders that present very high / low prices. We checked some of the empirical stylized facts pointed out by Cont (2001), among others enshrined in the microstructure literature. In general, the stylized facts were replicated by the data. We apply the filter, proposed by Brownlees and Gallo (2006), to Petrobras' stock and we analyze the sensitivity of the number of possible outliers found by the filter with respect to the filter's parameters variation. We propose using the Akaike criterion to sort and select conditional duration models whose samples have different length sizes. The selected models are not always those in which the data has been filtered. For the ACD (1,1) setting, when we consider only well-adjusted models, the Akaike criterion indicates as better model as one in which the data were not filtered.<br>O presente trabalho utiliza os dados disponibilizados pela BM&FBovespa para analisar as ações da Petrobrás para os meses compreendidos entre julho e agosto de 2010 e outubro de 2008. Primeiramente, apresentamos uma discussão detalhada sobre a manipulação desses dados, na qual explicitamos a impossibilidade de se usar o mid-price quote devido ao número elevado de ofertas de compra/venda com preços muito elevados/baixos. Verificamos alguns dos fatos estilizados empíricos apontados por Cont (2001), entre outros consagrados na literatura de microestrutura. Em geral, os dados replicaram os fatos estilizados. Aplicamos o filtro proposto por Brownlees e Gallo (2006) às ações da Petrobrás e analisamos a sensibilidade do número de possíveis outliers encontrados pelo filtro a variação dos parâmetros desse filtro. Propomos utilizar o critério de Akaike para ordenar e selecionar modelos de duração condicional cujas amostras de duração possuem tamanhos distintos. Os modelos selecionados, nem sempre são aqueles em que os dados foram filtrados. Para o ajuste ACD (1,1), quando considerados apenas os modelos bem ajustados (resíduos não autocorrelacionados), o critério de Akaike indica como melhor modelo aquele em que os dados não foram filtrados.
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Zhang, Q., Charlie X. Cai, and K. Keasey. "Forecasting using high-frequency data: a comparison of asymmetric financial duration models." 2009. http://hdl.handle.net/10454/6250.

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The first purpose of this paper is to assess the short-run forecasting capabilities of two competing financial duration models. The forecast performance of the Autoregressive Conditional Multinomial–Autoregressive Conditional Duration (ACM-ACD) model is better than the Asymmetric Autoregressive Conditional Duration (AACD) model. However, the ACM-ACD model is more complex in terms of the computational setting and is more sensitive to starting values. The second purpose is to examine the effects of market microstructure on the forecasting performance of the two models. The results indicate that the forecast performance of the models generally decreases as the liquidity of the stock increases, with the exception of the most liquid stocks. Furthermore, a simple filter of the raw data improves the performance of both models. Finally, the results suggest that both models capture the characteristics of the micro data very well with a minimum sample length of 20 days.
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Voráčková, Andrea. "Modelování durací mezi finančními transakcemi." Master's thesis, 2018. http://www.nusl.cz/ntk/nusl-372948.

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Lee, Ji-Yuan, and 李季遠. "Applying the family of ACD model to analysis the autocorrelation of the price-limited duration." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/7ej6eq.

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碩士<br>銘傳大學<br>財務金融學系碩士班<br>96<br>As the ACD models develop over time, many related issues have been widely explored. In this study, we apply the augmented ACD family and the smooth transition ACD model proposed by Fernandes(2006) and Meitz(2006) to empirically analyze the price-limited duration data from Taiwan equity market .The empirical results of this study indicate that the price-limited durations exhibited high autocorrelation .The parameters estimated in this study indicate that there are substantial diversities between different ACD models .Based on the values of log-likelihood function, AIC and SBIC, the smooth transition ACD model has relatively better performance. It shows the ACD model with the construct of threshold has better performance to catch the dynamic process of the price-limited duration.
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Hu, Mingming. "Financial Time Series Models and Applications." 2011. http://hdl.handle.net/1993/4373.

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Duration models are often concerned with time intervals between trades, longer durations indicating a lack of trading activities. In this thesis, we study parameter estimation for the Autoregressive Conditional Duration (ACD) and Stochastic Conditional Duration (SCD) models. Maximum likelihood methods can usually be used in the case of ACD models. However, the SCD models are based on the assumption that durations are generated by a dynamic stochastic latent variable which is often perturbed by Exponential, Weibull, Gamma or Log-Normal distributed innovations. This makes the use of maximum likelihood methods difficult. One alternative method of parameter estimation, in this case, consists in using quasi-maximum likelihood after transforming the original nonlinear model into a state-space model and using the Kalman filter, a similar filtering scheme or the Generalized Method of Moments (GMM). We use the nonlinear filter and GMM method to analyze the Quadratic Stochastic Conditional duration model as well.
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Lemyre, Gabriel. "Modèles de Markov à variables latentes : matrice de transition non-homogène et reformulation hiérarchique." Thesis, 2021. http://hdl.handle.net/1866/25476.

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Ce mémoire s’intéresse aux modèles de Markov à variables latentes, une famille de modèles dans laquelle une chaîne de Markov latente régit le comportement d’un processus stochastique observable à travers duquel transparaît une version bruitée de la chaîne cachée. Pouvant être vus comme une généralisation naturelle des modèles de mélange, ces processus stochastiques bivariés ont entre autres démontré leur faculté à capter les dynamiques variables de maintes séries chronologiques et, plus spécifiquement en finance, à reproduire la plupart des faits stylisés des rendements financiers. Nous nous intéressons en particulier aux chaînes de Markov à temps discret et à espace d’états fini, avec l’objectif d’étudier l’apport de leurs reformulations hiérarchiques et de la relaxation de l’hypothèse d’homogénéité de la matrice de transition à la qualité de l’ajustement aux données et des prévisions, ainsi qu’à la reproduction des faits stylisés. Nous présentons à cet effet deux structures hiérarchiques, la première permettant une nouvelle interprétation des relations entre les états de la chaîne, et la seconde permettant de surcroît une plus grande parcimonie dans la paramétrisation de la matrice de transition. Nous nous intéressons de plus à trois extensions non-homogènes, dont deux dépendent de variables observables et une dépend d’une autre variable latente. Nous analysons pour ces modèles la qualité de l’ajustement aux données et des prévisions sur la série des log-rendements du S&P 500 et du taux de change Canada-États-Unis (CADUSD). Nous illustrons de plus la capacité des modèles à reproduire les faits stylisés, et présentons une interprétation des paramètres estimés pour les modèles hiérarchiques et non-homogènes. Les résultats obtenus semblent en général confirmer l’apport potentiel de structures hiérarchiques et des modèles non-homogènes. Ces résultats semblent en particulier suggérer que l’incorporation de dynamiques non-homogènes aux modèles hiérarchiques permette de reproduire plus fidèlement les faits stylisés—même la lente décroissance de l’autocorrélation des rendements centrés en valeur absolue et au carré—et d’améliorer la qualité des prévisions obtenues, tout en conservant la possibilité d’interpréter les paramètres estimés.<br>This master’s thesis is centered on the Hidden Markov Models, a family of models in which an unobserved Markov chain dictactes the behaviour of an observable stochastic process through which a noisy version of the latent chain is observed. These bivariate stochastic processes that can be seen as a natural generalization of mixture models have shown their ability to capture the varying dynamics of many time series and, more specifically in finance, to reproduce the stylized facts of financial returns. In particular, we are interested in discrete-time Markov chains with finite state spaces, with the objective of studying the contribution of their hierarchical formulations and the relaxation of the homogeneity hypothesis for the transition matrix to the quality of the fit and predictions, as well as the capacity to reproduce the stylized facts. We therefore present two hierarchical structures, the first allowing for new interpretations of the relationships between states of the chain, and the second allowing for a more parsimonious parameterization of the transition matrix. We also present three non-homogeneous models, two of which have transition probabilities dependent on observed explanatory variables, and the third in which the probabilities depend on another latent variable. We first analyze the goodness of fit and the predictive power of our models on the series of log returns of the S&P 500 and the exchange rate between canadian and american currencies (CADUSD). We also illustrate their capacity to reproduce the stylized facts, and present interpretations of the estimated parameters for the hierarchical and non-homogeneous models. In general, our results seem to confirm the contribution of hierarchical and non-homogeneous models to these measures of performance. In particular, these results seem to suggest that the incorporation of non-homogeneous dynamics to a hierarchical structure may allow for a more faithful reproduction of the stylized facts—even the slow decay of the autocorrelation functions of squared and absolute returns—and better predictive power, while still allowing for the interpretation of the estimated parameters.
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Books on the topic "Autoregressive duration model (ACD)"

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Engle, R. F. Forecasting transaction rates: The autoregressive conditional duration model. National Bureau of Economic Research, 1994.

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McCleary, Richard, David McDowall, and Bradley J. Bartos. Intervention Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0005.

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The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.
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Book chapters on the topic "Autoregressive duration model (ACD)"

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Hautsch, Nikolaus. "Autoregressive Conditional Duration Models." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17015-7_5.

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Zheng, Yao, Yang Li, Wai Keung Li, and Guodong Li. "Diagnostic Checking for Weibull Autoregressive Conditional Duration Models." In Advances in Time Series Methods and Applications. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6568-7_4.

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Yatigammana, Rasika P., S. T. Boris Choy, and Jennifer S. K. Chan. "Autoregressive Conditional Duration Model with an Extended Weibull Error Distribution." In Causal Inference in Econometrics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27284-9_5.

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Duchesne, Pierre, and Yongmiao Hong. "On Diagnostic Checking Autoregressive Conditional Duration Models with Wavelet-Based Spectral Density Estimators." In Advances in Time Series Methods and Applications. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6568-7_3.

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"How well can autoregressive duration models capture the price durations dynamics of foreign exchanges?" In Information Spillover Effect and Autoregressive Conditional Duration Models. Routledge, 2014. http://dx.doi.org/10.4324/9781315768847-13.

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"Extreme risk spillover between Chinese stock markets and international stock markets." In Information Spillover Effect and Autoregressive Conditional Duration Models. Routledge, 2014. http://dx.doi.org/10.4324/9781315768847-11.

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"Methodology to detect extreme risk spillover." In Information Spillover Effect and Autoregressive Conditional Duration Models. Routledge, 2014. http://dx.doi.org/10.4324/9781315768847-9.

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"Introduction." In Information Spillover Effect and Autoregressive Conditional Duration Models. Routledge, 2014. http://dx.doi.org/10.4324/9781315768847-8.

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"Information spillover effects between Chinese futures market and spot market." In Information Spillover Effect and Autoregressive Conditional Duration Models. Routledge, 2014. http://dx.doi.org/10.4324/9781315768847-12.

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"Conclusions and perspective studies." In Information Spillover Effect and Autoregressive Conditional Duration Models. Routledge, 2014. http://dx.doi.org/10.4324/9781315768847-15.

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Conference papers on the topic "Autoregressive duration model (ACD)"

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Miao, Junhong, and Yanying Wang. "A Family of Autoregressive Conditional Duration Model under Random Environment." In 2011 4th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2011. http://dx.doi.org/10.1109/iscid.2011.129.

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Liu, Li-Juan, Yan-Nian Chen, Jing-Xuan Zhang, et al. "Non-Parallel Voice Conversion with Autoregressive Conversion Model and Duration Adjustment." In Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020. ISCA, 2020. http://dx.doi.org/10.21437/vcc_bc.2020-17.

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Ma, Yulin, and Yuan Zhao. "Research on the Trade Duration of HS300 Index Based on ACD Model." In 2013 Ninth International Conference on Computational Intelligence and Security (CIS). IEEE, 2013. http://dx.doi.org/10.1109/cis.2013.164.

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Vorwald, John, Alan Schwartz, and Christopher Kent. "Near Term Ship Motion Forecasting From Prior Motion." In ASME 2016 Fluids Engineering Division Summer Meeting collocated with the ASME 2016 Heat Transfer Summer Conference and the ASME 2016 14th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/fedsm2016-7781.

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Ship-motion forecasting can be useful for naval operations such as aircraft landing, cargo transfer, off-loading of small boats, and ship “mating” between a large transport ship and smaller ships. The forecasted ship motion is particularly useful in sea states above SS3 when unanticipated large motions can suddenly occur. A 5- to 10-second forecast of future ship motion provides the operator time to compensate for the motion to avoid serious collisions. Ship motion forecasting can enable autonomous landing during higher sea states and can also provide an alternative ship location estimate during emergency procedures such as loss of communication link. This paper summarizes the evaluation of four categories of forecasting methodologies: subspace algorithms, autoregressive algorithms, nonlinear autoregressive using a wavelet network, and perturbation error methods. Simulated Model 5415 ship motion was evaluated in 24 conditions including sea states 4–6, ship speeds of 5, 10, 20, and 30 kts, and wave headings 150 and 180 deg (bow and head seas). For each condition, the simulation motion data was divided into twenty 7.5-minute segments consisting of 0.5 to 5 minutes of training data and 2.5 minutes of testing data. Two types of forecasting accuracy metrics were developed. One metric was based on forecasting simulated ship motion and the other metric involved forecasting periods when the motion exceeds threshold limits within a 4-second window, representing a quiescent period. The results indicate that for simulation forecast accuracy, the correlation coefficient between forecasted and actual motion was greater than 80% for 5-sec forecast horizons, and greater than 60% for 10-sec forecast horizons. For motion threshold forecasting, the forecasting accuracy was greater than 90% for 5-sec horizons and greater than 60% for 10-sec horizons. A qualitative assessment of both simulation and threshold metrics indicated that 80% accuracy produces a good forecast and 60% accuracy produces an acceptable forecast. Threshold forecasting can forecast the presence and duration of near-future quiescent periods, enabling safer, more efficient operations and reduced cost of ship-based aviation operations such as launch, recovery, and movement of aircraft.
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Elias, Isaac, Heiga Zen, Jonathan Shen, et al. "Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling." In Interspeech 2021. ISCA, 2021. http://dx.doi.org/10.21437/interspeech.2021-1461.

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Hou, Yafei, Yusuke Tanaka, Julian Webber, Kazuto Yano, Satoshi Denno, and Tomoaki Kumagai. "Busy/Idle Duration Model for WLAN Traffic and its Prediction Performance Using Autoregressive Method." In 2018 Asia-Pacific Microwave Conference (APMC). IEEE, 2018. http://dx.doi.org/10.23919/apmc.2018.8617663.

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Li, Yichen, Jing Gong, Weichao Yu, Weihe Huang, and Kai Wen. "Gas Supply Reliability Analysis of a Natural Gas Pipeline System Considering the Effects of Demand Side Management." In ASME 2020 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/pvp2020-21218.

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Abstract At present, China has a developing natural gas market, and ensuring the security of gas supply is an issue of high concern. Gas supply reliability, the natural gas pipeline system’s ability to satisfy the market demand, is determined by both supply side and demand side, and is usually adopted by the researches to measure the security of gas supply. In the previous study, the demand side is usually simplified by using load duration curve (LDC) to describe the demand, which neglects the effect of demand side management. The simplification leads to the inaccurate and unreasonable assessment of the gas supply reliability, especially in high demand situation. To overcome this deficiency and achieve a more reasonable result of gas supply reliability, this paper extends the previous study on demand side by proposing a novel method of management on natural gas demand side, and the effects of demand side management on gas supply reliability is analyzed. The management includes natural gas prediction models for different types of users, the user classification rule, and the demand adjustment model based on user classification. Firstly, An autoregressive integrated moving average (ARIMA) model and a support vector machine (SVM) model are applied to predict the natural gas demand for different types of users, such as urban gas distributor (including residential customer, commercial customer, small industrial customer), power plant, large industrial customer, and Compressed Natural Gas (CNG) station. Then, the user classification rule is built based on users’ attribute and impact of supplied gas’s interruption or reduction. Natural gas users are classified into four levels. (1) Demand Fully Satisfied; (2) Demand Slightly Reduced; (3) Demand Reduced; (4) Demand Interrupted. The user classification rule also provides the demand reduction range of different users. Moreover, the optimization model of demand adjustment is built, and the objective of the model is to maximize the amount of gas supply for each user based on the classification rule. The constraints of the model are determined by the classification rule, including the demand reduction range of different users. Finally, the improved method of gas supply reliability assessment is developed, and is applied to the case study of our previous study derived from a realistic natural gas pipeline system operated by PetroChina to analyze the effects of demand side management on natural gas pipeline system’s gas supply reliability.
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Reports on the topic "Autoregressive duration model (ACD)"

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Engle, Robert, and Jeffrey Russell. Forecasting Transaction Rates: The Autoregressive Conditional Duration Model. National Bureau of Economic Research, 1994. http://dx.doi.org/10.3386/w4966.

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