Relevant bibliographies by topics / Time series demand model

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Time series demand model'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Time series demand model"

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Moore, Ian C., David P. Strum, Luis G. Vargas, and David J. Thomson. "Observations on Surgical Demand Time Series." Anesthesiology 109, no. 3 (2008): 408–16. http://dx.doi.org/10.1097/aln.0b013e318182a955.

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Background Surgical scheduling is complicated by both naturally occurring and human-induced variability in the demand for surgical services. Surgical demand time series are decomposed into periodic, lagged, and linear trends with frequent occurrences of nonconstant variations in mean and variance. The authors used time series methods to model surgical demand time series in order to improve the scheduling of scarce surgical resources. Methods With institutional approval, the authors studied 47,752 surgeries undertaken at a large academic medical center. They initially extracted periodic informa
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Vaziri, Manouchehr, John Hutchinson, and Mohammad Kermanshah. "Short‐Term Demand for Specialized Transportation: Time‐Series Model." Journal of Transportation Engineering 116, no. 1 (1990): 105–21. http://dx.doi.org/10.1061/(asce)0733-947x(1990)116:1(105).

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Brahimi, Tahar, and Tahar Smain. "A Nonstationary Mathematical Model for Acceleration Time Series." Mathematical Modelling of Engineering Problems 8, no. 2 (2021): 246–52. http://dx.doi.org/10.18280/mmep.080211.

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The choice of nonstationary stochastic models for the study is fully justified by the limitation of acceleration time series number. The three acceleration time series under consideration are used to generate a new, artificial series of ten per historical one using autoregressive moving average model. Subsequently, the average of nonlinear is utilized for the ten acceleration time series in order to obtain the spectral response of a system with single degree of freedom. Modeling of acceleration time series involves critical estimation of metrics that characterize nonstationary acceleration tim
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Cyprich, Ondrej, Vladimír Konečný, and Katarína Kiliánová. "Short-Term Passenger Demand Forecasting Using Univariate Time Series Theory." PROMET - Traffic&Transportation 25, no. 6 (2013): 533–41. http://dx.doi.org/10.7307/ptt.v25i6.338.

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The purpose of the paper is to identify and analyse the forecasting performance of the model of passenger demand for suburban bus transport time series, which satisfies the statistical significance of its parameters and randomness of its residuals. Box-Jenkins, exponential smoothing and multiple linear regression models are used in order to design a more accurate and reliable model compared the ones used nowadays. Forecasting accuracy of the models is evaluated by comparative analysis of the calculated mean absolute percent errors of different approaches to forecasting. In accordance with the
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Xue, Rui, Daniel (Jian) Sun, and Shukai Chen. "Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach." Discrete Dynamics in Nature and Society 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/682390.

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Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM) filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly,
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Sakhuja, Sumit, Vipul Jain, Sameer Kumar, Charu Chandra, and Sarit K. Ghildayal. "Genetic algorithm based fuzzy time series tourism demand forecast model." Industrial Management & Data Systems 116, no. 3 (2016): 483–507. http://dx.doi.org/10.1108/imds-05-2015-0165.

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Purpose – Many studies have proposed variant fuzzy time series models for uncertain and vague data. The purpose of this paper is to adapt a fuzzy time series combined with genetic algorithm (GA) to forecast tourist arrivals in Taiwan. Design/methodology/approach – Different cases are studied to understand the effect of variation of fuzzy time series order, number of intervals and population size on the fitness function which decreases with increase in fuzzy time series order and number of fuzzy intervals, but do not have marginal effect due to change in population size. Findings – Results base
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Ismail, Z., F. Jamaluddin, and F. Jamaludin. "Time Series Regression Model for Forecasting Malaysian Electricity Load Demand." Asian Journal of Mathematics & Statistics 1, no. 3 (2008): 139–49. http://dx.doi.org/10.3923/ajms.2008.139.149.

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Ruiz Reina, Miguel Ángel. "Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand." Engineering Proceedings 5, no. 1 (2021): 17. http://dx.doi.org/10.3390/engproc2021005017.

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In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the hig
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TSUTSUMI, Masafumi, and Takeshi CHISHAKI. "Time-series prediction system and AROP model in transportation demand analysis." Doboku Gakkai Ronbunshu, no. 407 (1989): 17–26. http://dx.doi.org/10.2208/jscej.1989.407_17.

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Choi, Bo-Seung, Hyun-Cheol Kang, Kyung-Yun Lee, and Sang-Tae Han. "A Development of Time-Series Model for City Gas Demand Forecasting." Korean Journal of Applied Statistics 22, no. 5 (2009): 1019–32. http://dx.doi.org/10.5351/kjas.2009.22.5.1019.

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Dissertations / Theses on the topic "Time series demand model"

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Oluwole, Oluwadamilola. "Weather-sensitive, spatially-disaggregated electricity demand model for Nigeria." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33043.

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The historical underinvestment in power infrastructure and the poor performance of power delivery has resulted in extensive and regular power shortages in Nigeria. As Nigeria aims to bridge its power supply gap, the recent deregulation of its electricity market has seen the privatisation of its generation and distribution companies. Ambitious plans have also been put in place to expand the transmission network and the total power generation capacity. However, these plans have been developed with essentially arbitrary estimates for prevailing demand levels as the network and generation limits m
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Chen, Jinduan. "Stochastic Demand-hydraulic Model of Water Distribution Systems." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439301579.

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Ninomiya, Yasushi. "The underlying energy demand trend and seasonality : an application of the structural time series model to energy demand in the UK and Japan." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/804880/.

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Hellström, Jörgen. "Count data modelling and tourism demand." Doctoral thesis, Umeå universitet, Institutionen för nationalekonomi, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-82168.

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This thesis consists of four papers concerning modelling of count data and tourism demand. For three of the papers the focus is on the integer-valued autoregressive moving average model class (INARMA), and especially on the ENAR(l) model. The fourth paper studies the interaction between households' choice of number of leisure trips and number of overnight stays within a bivariate count data modelling framework. Paper [I] extends the basic INAR(1) model to enable more flexible and realistic empirical economic applications. The model is generalized by relaxing some of the model's basic independe
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Svensk, Gustav. "TDNet : A Generative Model for Taxi Demand Prediction." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158514.

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Supplying the right amount of taxis in the right place at the right time is very important for taxi companies. In this paper, the machine learning model Taxi Demand Net (TDNet) is presented which predicts short-term taxi demand in different zones of a city. It is based on WaveNet which is a causal dilated convolutional neural net for time-series generation. TDNet uses historical demand from the last years and transforms features such as time of day, day of week and day of month into 26-hour taxi demand forecasts for all zones in a city. It has been applied to one city in northern Europe and on
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Mohammadipour, Maryam. "Intermittent demand forecasting with integer autoregressive moving average models." Thesis, Bucks New University, 2009. http://bucks.collections.crest.ac.uk/9586/.

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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 Cros
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Kim, Youngtae. "Development of a model to examine the determinants of demand for international hotel rooms in Seoul." Diss., This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-06062008-160627/.

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Arandia, Ernesto. "Spatial-Temporal Statistical Modeling of Treated Drinking Water Usage." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377870978.

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Nigrini, Lucas Bernardo. "Developing a neural network model to predict the electrical load demand in the Mangaung municipal area." Thesis, [Bloemfontein?] : Central University of Technology, Free State, 2012. http://hdl.handle.net/11462/176.

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Thesis (D. Tech. (Engineering: Electric)) -- Central University of technology, 2012<br>Because power generation relies heavily on electricity demand, consumers are required to wisely manage their loads to consolidate the power utility‟s optimal power generation efforts. Consequently, accurate and reliable electric load forecasting systems are required. Prior to the present situation, there were various forecasting models developed primarily for electric load forecasting. Modelling short term load forecasting using artificial neural networks has recently been proposed by researchers.
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Atallah, Tarek. "Measuring the Transition toward Less Energy Intensive Economies : modeling Solutions for the Demand-Side." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLED024.

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Le monde est actuellement confronté à une transition du marché de l'énergie qui est influencée notamment par la dynamique de la croissance économique globale, les négociations relatives aux changements climatiques et des prix de plus en plus volatils. Cette évolution rapide des réglementations et de la macro-économie transformera les conditions de la demande d'énergie, obligeant les gouvernements à acquérir un ensemble croissant d'outils quantitatifs pour mieux évaluer les résultats de leurs politiques fiscales. Cette thèse aborde cette problématique en analysant, par une approche basée sur le
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Books on the topic "Time series demand model"

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Semlali, A. Senhadji. Time series analysis of export demand equations: A cross-country analysis. International Monetary Fund, IMF Institute, 1998.

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Rüdel, Thomas. Kointegration und Fehlerkorrekturmodelle: Mit einer empirischen Untersuchung zur Geldnachfrage in der Bundesrepublik Deutschland. Physica-Verlag, 1989.

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Melni*k, Rafi. Financial services, cointegration and the demand for money in Israel. Research Dept., Bank of Israel, 1992.

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Mincer, Jacob. Human capital, technology, and the wage structure: What do time series show? National Bureau of Economic Research, 1991.

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Bordo, Michael D. The common development of institutional change as measured by income velocity: A century of evidence from industrialized countries. National Bureau of Economic Research, 1993.

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Harvey, Andrew. Multivariate structural time series model. Suntory and ToyotaInternational Centres for Economics and Related Disciplines, 1996.

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

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Dehn, Jan. Cointegration time series analysis of aggregate import demand. typescript, 1994.

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Callen, Mindy. Time series tests of the Ohlson model. UMI Dissertation Services, 1999.

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

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Book chapters on the topic "Time series demand model"

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Chou, Hung-Lieh, Jr-Shian Chen, Ching-Hsue Cheng, and Hia Jong Teoh. "Forecasting Tourism Demand Based on Improved Fuzzy Time Series Model." In Intelligent Information and Database Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12145-6_41.

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García-Díaz, J. Carlos, and Óscar Trull. "Competitive Models for the Spanish Short-Term Electricity Demand Forecasting." In Time Series Analysis and Forecasting. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28725-6_17.

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Peacock, Malcolm, Aikaterini Fragaki, and Bogdan J. Matuszewski. "Review of Heat Demand Time Series Generation for Energy System Modelling." In Springer Proceedings in Energy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_7.

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AbstractNational heat demand time series are important inputs into national energy system models. Although time series for primary fuel such as gas might be available, heat demand is not and measuring heat demand is only possible for individual buildings. Four different methods are used in this work to generate daily heat demand time series for Great Britain for 2016–2018 from temperature and windspeed and are validated against heat demand derived from national grid gas demand. All seem to model heat demand well.
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Tang, Jiechen, Songsak Sriboonchitta, and Xinyu Yuan. "Forecasting Inbound Tourism Demand to China Using Time Series Models and Belief Functions." In Econometrics of Risk. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13449-9_23.

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Singh, Nikita, Pratyush Sharma, Nikhil Kumar, and Mini Sreejeth. "Short-Term Load Forecasting Using Artificial Neural Network and Time Series Model to Predict the Load Demand for Delhi and Greater Noida Cities." In Proceedings of 6th International Conference on Recent Trends in Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4501-0_41.

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Berk, Kevin. "Time series analysis." In Modeling and Forecasting Electricity Demand. Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-08669-5_3.

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Kopetz, Hermann. "Real-Time Model." In Real-Time Systems Series. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-8237-7_4.

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Capiński, Marek, and Tomasz Zastawniak. "Continuous Time Model." In Springer Undergraduate Mathematics Series. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-082-3_8.

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Rickertsen, Kyrre, and Per Halvor Vale. "Household and Aggregate Time-Series Data." In The Econometrics of Demand Systems. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1277-2_9.

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Taylor, Lester D. "Discussion of the Time-Series Results." In Consumer Demand in the United States. Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0510-9_16.

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Conference papers on the topic "Time series demand model"

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Stoimenova, E., K. Prodanova, R. Prodanova, and Michail D. Todorov. "Forecasting Electricity Demand by Time Series Models." In APPLICATIONS OF MATHEMATICS IN ENGINEERING AND ECONOMICS' 33: 33rd International Conference. AIP, 2007. http://dx.doi.org/10.1063/1.2806042.

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Shen, Wen, Vahan Babushkin, Zeyar Aung, and Wei Lee Woon. "An ensemble model for day-ahead electricity demand time series forecasting." In the the fourth international conference. ACM Press, 2013. http://dx.doi.org/10.1145/2487166.2487173.

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Yan-Chen, Wang, Zhang De-Gang, and Wang Xu. "Prediction model of supply chain demand based on fuzzy neural network with chaotic time series." In 2013 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). IEEE, 2013. http://dx.doi.org/10.1109/soli.2013.6611457.

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Wang Xu, Jia Yan-min, and Li Hui. "Prediction model of supply chain demand based on fuzzy neural network with chaotic time series." In 2009 IEEE/INFORMS International Conference on Service Operations, Logistics and Informatics (SOLI). IEEE, 2009. http://dx.doi.org/10.1109/soli.2009.5204018.

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Cheng, Fengjiao, and Ruoying Sun. "Inventory demand forecast based on gray correlation analysis and time series neural network hybrid model." In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017. http://dx.doi.org/10.1109/fskd.2017.8393167.

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Rabbi, Fazly, Shihab Uddin Tareq, Md Monirul Islam, Mohammad Asaduzzaman Chowdhury, and Mohammad Abul Kashem. "A Multivariate Time Series Approach for Forecasting of Electricity Demand in Bangladesh Using ARIMAX Model." In 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI). IEEE, 2020. http://dx.doi.org/10.1109/sti50764.2020.9350326.

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Brahimi, Malek, Sidi Berri, Abdelrazak Menasseri, and Abdelrachid Boulaouad. "The Use of Stochastic Models to Measure Damage Potential in Acceleration Time Series." In 17th International Conference on Nuclear Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/icone17-75349.

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ARMA models are obtained with parameters chosen to fit real accelerograms of different earthquake records. The maximum likelihood technique is used to estimate the parameters. A random set of earthquakes is generated for each event and used to establish statistically valid structural response spectra. From a sample of earthquakes, the mean and variance of response spectral ordinates are obtained for damage predictors namely peak linear displacement, ductility demand and hysteretic energy demand and compared to spectra based on single earthquake records. These Models are used to assess the dama
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K. da Costa, Guinther, Leandro Dos S. Coelho, and Roberto Z. Freire1. "Image Representation of Time Series for Reinforcement Learning Trading Agent." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1108.

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The availability of diverse data has increased the demand for expertise in algorithmic trading strategies. Reinforcement learning has shown interesting applicability in a wide range of tasks, especially in some challenging problems as trading, where slow model convergence, inference speed, and reduced model accuracy appear as barriers in this type of application. In this paper, we propose the transformation of time series into images considering a transfer learning based on a semi-supervised model with deep Q learning agents, where labels were generated by an evolutionary algorithm to improve
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Qiu, Xueheng, P. N. Suganthan, and Gehan A. J. Amaratunga. "Electricity load demand time series forecasting with Empirical Mode Decomposition based Random Vector Functional Link network." In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2016. http://dx.doi.org/10.1109/smc.2016.7844431.

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Chen, Pudi, Shenghua Liu, Chuan Shi, Bryan Hooi, Bai Wang, and Xueqi Cheng. "NeuCast: Seasonal Neural Forecast of Power Grid Time Series." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/460.

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In the smart power grid, short-term load forecasting (STLF) is a crucial step in scheduling and planning for future load, so as to improve the reliability, cost, and emissions of the power grid. Different from traditional time series forecast, STLF is a more challenging task, because of the complex demand of active and reactive power from numerous categories of electrical loads and the effects of environment. Therefore, we propose NeuCast, a seasonal neural forecasting method, which dynamically models various loads as co-evolving time series in a hidden space, as well as extra weather conditio
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Reports on the topic "Time series demand model"

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

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Linton, Oliver, Zudi Lu, Degui Li, and Jia Chen. Semiparametric model averaging of ultra-high dimensional time series. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.cem.2015.6215.

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Rosen, Sherwin, and Robert Topel. A Time-Series Model of Housing Investment in the U.S. National Bureau of Economic Research, 1986. http://dx.doi.org/10.3386/w1818.

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Tang, Philip. Stochastic Hydrologic Modeling in Real Time Using a Deterministic Model (Streamflow Synthesis and Reservoir Regulation Model), Time Series Model, and Kalman Filter. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6464.

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Czaplewski, Raymond L., and Mike T. Thompson. Model-based time-series analysis of FIA panel data absent re-measurements. 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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Senin, Pavel, and Sergey Malinchik. SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada603196.

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Giovannini, Alberto, and Philippe Jorion. Time-Series Tests of a Non-Expected-Utility Model of Asset Pricing. National Bureau of Economic Research, 1989. http://dx.doi.org/10.3386/w3195.

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Singleton, Kenneth. Asset Prices in a Time Series Model with Disparately Informed, Competative Traders. National Bureau of Economic Research, 1986. http://dx.doi.org/10.3386/w1897.

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Kim, Changmo, Ghazan Khan, Brent Nguyen, and Emily L. Hoang. Development of a Statistical Model to Predict Materials’ Unit Prices for Future Maintenance and Rehabilitation in Highway Life Cycle Cost Analysis. Mineta Transportation Institute, 2020. http://dx.doi.org/10.31979/mti.2020.1806.

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The main objectives of this study are to investigate the trends in primary pavement materials’ unit price over time and to develop statistical models and guidelines for using predictive unit prices of pavement materials instead of uniform unit prices in life cycle cost analysis (LCCA) for future maintenance and rehabilitation (M&amp;R) projects. Various socio-economic data were collected for the past 20 years (1997–2018) in California, including oil price, population, government expenditure in transportation, vehicle registration, and other key variables, in order to identify factors affecting
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Bleasdale, S. A., T. L. Burr, J. C. Scovel, and R. B. Strittmatter. Knowledge fusion: An approach to time series model selection followed by pattern recognition. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/219426.

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