Tesis sobre el tema "Analytic Network Proce"
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Winicki, Elliott. "ELECTRICITY PRICE FORECASTING USING A CONVOLUTIONAL NEURAL NETWORK". DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2126.
Texto completoAkra, Abraham. "Modelling the Four-Party Billing Payment Scheme: The Case of BPAY". Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9515.
Texto completoWhitehurst, Jonathan. "A network probe for the mode analysis of planar antennas". Thesis, Royal Holloway, University of London, 1988. http://repository.royalholloway.ac.uk/items/d6571ade-592e-4539-86de-a912a5a79238/1/.
Texto completoBaccouche, Alexandre. "Functional analysis of artificial DNA reaction network". Thesis, Sorbonne Paris Cité, 2015. http://www.theses.fr/2015USPCB135/document.
Texto completoInformation processing within and in between living organisms involves the production and exchange of molecules through signaling pathways organized in chemical reactions networks. They are various by their shape, size, and by the nature of the molecules embroiled. Among them, gene regulatory networks were our inspiration to develop and implement a new framework for in-vitro molecular programming. Indeed, the expression of a gene is mostly controlled by transcription factors or regulatory proteins and/or nucleic acids that are themselves triggered by other genes. The whole assembly draws a web of cross-interacting genes and their subproducts, in which the well controlled topology relates to a precise function. With a closer look at the links between nodes in such architectures, we identify three key points in the inner operating system. First, the interactions either activate or inhibit the production of the later node, meaning that non trivial behaviors are obtained by a combination of nodes rather than a specific new interaction. Second, the chemical stability of DNA, together with the precise reactivity of enzymes ensures the longevity of the network. Finally, the dynamics are sustained by the constant anabolism/catabolism of the effectors, and the subsequent use of fuel/energy. All together, these observations led us to develop an original set of 3 elementary enzymatic reactions: the PEN-DNA toolbox. The architecture of the assembly, i.e. the connectivity between nodes relies on the sequence of synthetic DNA strands (called DNA templates), and 3 enzymes (a polymerase, a nickase and an exonuclease) are taking care of catalysis. The production and degradation of intermediates consume deoxyribonucleoside triphosphates (dNTP) and produce deoxynucleotide monophosphates leading to the dissipation of chemical potential. Reactions are monitored thanks to a backbone modification of a template with a fluorophore and the nucleobase quenching effect consecutive to an input strand binding the template. The activation mechanism is then the production of an output following the triggering of an input strand, and the inhibition comes from the production of an output strand that binds the activator-producing sequence. Various behaviors such as oscillation, bistability, or switchable memory have been implemented, requiring more and more complex topologies. For that, each circuit requires a fine tuning in the amount of chemical parameters, such as templates and enzymes. This underlies the fact that a given network may lead to different demeanors depending on the set of parameters. Mapping the output of each combination in the parameter space to find out the panel of behaviors leads to the bifurcation diagram of the system. In order to explore exhaustively the possibilities of one circuit with a reasonable experimental cost, we developped a microfluidic tool generating picoliter-sized water-in-oil droplets with different contents. We overcame the technical challenges in hardware (microfluidic design, droplet generation and long-term observation) and wetware (tracability of the droplet and emulsion compatibility/stability). So far, bifurcation diagrams were calculated from mathematical models based on the enzymes kinetics and the thermodynamic properties of each reaction. The model was then fitted with experimental data taken in distant points in the parameter space. Here, millions of droplets are created, and each one encloses a given amount of parameters, becoming one point in the diagram. The parameter coordinates are barcoded in the droplet, and the output fluorescence signal is recorded by time lapse microscopy. We first applied this technique to a well-known network, and obtained the first experimental two-dimensional bifurcation diagram of the bistable system. The diagram enlightens features that were not described by the previous mathematical model. (...)
Caufield, J. Harry. "Interactomics-Based Functional Analysis: Using Interaction Conservation To Probe Bacterial Protein Functions". VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4580.
Texto completoDravenstott, Ronald W. "Restaurant Industry Stock Price Forecasting Model Utilizing Artificial Neural Networks to Combine Fundamental and Technical Analysis". Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1337780178.
Texto completoSong, Fei. "Deregulated power transmission analysis and planning in congested networks". Thesis, Brunel University, 2008. http://bura.brunel.ac.uk/handle/2438/4819.
Texto completoRodrigues, Félix Carvalho. "Smoothed analysis in Nash equilibria and the Price of Anarchy". reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/54866.
Texto completoThis thesis analyzes problems in game theory with respect to perturbation. It uses smoothed analysis to accomplish such task and focuses on two kind of games, bimatrix games and the traffic assignment problem. The Lemke-Howson algorithm is a widely used algorithm to compute a Nash equilibrium of a bimatrix game. This problem is PPAD-complete (Polynomial Parity Arguments on Directed graphs), and there exists an instance which takes exponential time (with any starting pivot.) It has been proven that even with a smoothed analysis it is still exponential. However, no experimental study has been done to verify and evaluate in practice how the algorithm behaves in such cases. This thesis shows in detail how the current known worst-case instances are generated and shows that the performance of the algorithm on these instances, when perturbed, differs from the expected behavior proven in theory. The Traffic Assignment Problem models a situation in a road network where users want to travel from an origin to a destination. It can be modeled as a game using game theory, with a Nash equilibrium happening when users behave selfishly and an optimal social welfare being the best possible flow from a global perspective. We provide a new measure, which we call the Smoothed Price of Anarchy, based on the smoothed analysis of algorithms in order to analyze the effects of perturbation on the Price of Anarchy. Using this measure, we analyze the effects that perturbation has on the Price of Anarchy for real and theoretical instances for the Traffic Assignment Problem. We demonstrate that the Smoothed Price of Anarchy remains in the same order as the original Price of Anarchy for polynomial latency functions. Finally, we study benchmark instances in relation to perturbation.
Dusílek, Adam. "Oceňování dobrovolné práce při pořádání velké sportovní akce". Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-198013.
Texto completoOujezský, Václav. "Konvergované sítě a tomografie síťového provozu s využitím evolučních algoritmů". Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-320776.
Texto completoKusch, Katharina. "Beyond customer perception of price discrimination: A consumer behavior analysis and its implications on aviation revenue management". Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-262169.
Texto completoHadachi, Amnir. "Travel Time Estimation Using Sparsely Sampled Probe GPS Data in Urban Road Networks Context". Phd thesis, INSA de Rouen, 2013. http://tel.archives-ouvertes.fr/tel-00800203.
Texto completoBenhamiche, Amal. "Designing optical multi-band networks : polyhedral analysis and algorithms". Thesis, Paris 9, 2013. http://www.theses.fr/2013PA090075/document.
Texto completoIn this thesis we consider two capacitated network design (CND) problems, using OFDM multi-band technology. The first problem is related to single-layer network design with specific requirements. We give an ILP formulation for this problem and study the polyhedra associated with its arc-set restriction. We describe two families of facet defining inequalities. We devise a Branch-and-Cut algorithm for the problem. Next, we investigate the multilayer version of CND using OFDM technology. We propose several ILP formulations and study the polyhedron associated with the first (cut) formulation. We identify several classes of facets and discuss the related separation problem. We devise a Branch-and-Cut algorithm embedding valid inequalities of both single-layer and multilayer problems. The second formulation is compact, and holds a polynomial number of constraints and variables. Two further path formulations are given which yield two efficient Branch-and-Price algorithms for the problem
Ott, Marion [Verfasser] y S. [Akademischer Betreuer] Berninghaus. "Second-price proxy auctions in bidder-seller networks : a game theoretic and experimental analysis / Marion Ott. Betreuer: S. Berninghaus". Karlsruhe : KIT-Bibliothek, 2009. http://d-nb.info/1014223121/34.
Texto completoFarníková, Jana. "Studie průběhu zakázky podnikem". Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2013. http://www.nusl.cz/ntk/nusl-224179.
Texto completoDoiron, Meghan. "Information access, market trade and rural livelihoods in the Peruvian amazon: an analysis of communication networks and price uncertainty in riverine communities". Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=117178.
Texto completoLa connaissance des prix du marché est un élément important du processus décisionnel au sujet des moyens de subsistance pour les paysans en milieu rural à travers les pays en voie de développement. Ces derniers temps, son rôle est devenu omniprésent ces milieux, compte tenu du développement mondiale et l'accroissement de l'accessibilité de l'information, provoquée par les innovations technologiques, notamment internet et les téléphones mobiles. Dans cette étude, nous examinons les structures sociales traditionnelles qui facilitent l'accès aux prix du marché entre les producteurs ribereño dans quatre villages longeant la rivière Tahuayo dans le nord-est de l'Amazonie péruvienne. De plus, nous évaluons les facteurs qui contribuent à l'incertitude de l'information sur le marché des prix et les implications sur les stratégies de subsistance des ménages. La méthodologie comprend des statistiques sommaires, des analyses de réseau en utilisant NetDraw et Probit et des modèles de régression OLS. Les données ont été recueillies au cours de Juin et Août 2011 et comprenaient des entretiens informels avec les trois exploitants de bateaux de transport par la rivière Tahuayo, des observations des participants, des relevés quotidiens des prix du marché pour quatre produits de haute importance régionale (le yuca, la banane plantain, le aguaje et le charbon de bois) et des entrevues semi-structurées auprès des chefs de ménages (n = 70) dans les quatre villages études. Nous utilisons aussi des données sur les ménages recueillies entre Juin et Novembre 2010 auprès des mêmes ménages échantillonnés (n = 70), et des données historiques des prix du marché entre 1994 et 1996. Nos résultats démontrent que les prix des produits du marché de Belén sont très variables en raison de l'approvisionnement instable du marché, voire parfois imprévisible, ce qui contribue à l'incertitude de l'information au sein des producteurs ribereño. L'information sur les prix du marché est principalement transmise de bouche à oreille par les autres membres du village. La connaissance des prix du marché est variable et dépend de la personne qui a été au marché pour cette semaine. Enfin, posséder un téléphone n'a pas amélioré la connaissance des ménages des prix du marché, probablement en raison d'autres problèmes sous-jacents liés à l'utilisation d'un téléphone comme le manque d'infrastructures électriques, l'accessibilité des télécommunications et de l'absence d'un informateur à appeler à Belén.
Wang, Nancy. "Spectral Portfolio Optimisation with LSTM Stock Price Prediction". Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273611.
Texto completoDen nobelprisvinnande moderna portföjlteorin (MPT) är utan tvekan en av de mest framgångsrika investeringsmodellerna inom finansvärlden och investeringsstrategier. MPT antar att investerarna är mindre benägna till risktagande och approximerar riskexponering med variansen av tillgångarnasränteavkastningar. Nyckeln till en lyckad portföljförvaltning är därmed goda riskestimat och goda förutsägelser av tillgångspris. Riskestimering görs vanligtvis genom traditionella prissättningsmodellerna som tillåter risken att variera i tiden, dock inte i frekvensrummet. Denna begränsning utgör bland annat ett större fel i riskestimering. För att tackla med detta har intresset för tillämpningar av spektraanalys på finansiella tidsserier ökat de senast åren. Bland annat är ett nytt tillvägagångssätt för att behandla detta den nyintroducerade spektralportföljteorin och spektralfak- tormodellen som påvisade ökad portföljenprestanda genom spektralriskskattning [1][11]. Samtidigt har prediktering av aktierpriser länge varit en stor utmaning på grund av dess icke-linjära och icke-stationära egenskaper medan maskininlärning har kunnat använts för att lösa annars omöjliga uppgifter. Färska studier har påvisat signifikant resultat i aktieprisprediktering med hjälp av artificiella LSTM neurala nätverk [6][34]. Detta arbete undersöker kombinerade effekten av dessa två framsteg i ett portföljoptimeringsproblem genom att optimera en spektral portfölj med framtida avkastningar predikterade av ett LSTM neuralt nätverk. Arbetet börjar med matematisk härledningar och teoretisk introduktion och sedan studera portföljprestation som genereras av spektra risk, LSTM aktieprispredikteringen samt en kombination av dessa två. Resultaten visar på att LSTM-predikteringen ensam presterade bättre än kombinationen, vilket i sin tur presterade bättre än enbart spektralriskskattningen.
Djervbrant, Karl-Johan y Andreas Häggström. "A Study on Fingerprinting of Locally Assigned MAC-Addresses". Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-39749.
Texto completoMängden enheter som kommunicerar över WiFi ökar dagligen och idag bär de flesta människor en enhet med ett aktiverat WiFi-nätverkskort. Detta använder EffectSoft AB, ett företag i Halmstad till sin teknik Flow för att räkna mobila enheter. Noggrannheten för beräkningen är dock inte tillräckligt bra för att produkten ska kunna vara applicerbar på marknaden och därav handlar denna kandidatuppsatsen om beräkning av mobila enheter. Denna rapport presenterar de problem som man stöter på vid beräkning av mobila enheter som till exempel randomisering av MAC-Adresser. Den täcker även hur tillverkare inte är konsekventa i sin implementation av IEEE 802.11 standarden och hur detta kan utnyttjas genom tre metoderför beräkning av antal mobila enheter. Det fastställs att Control Frame Attack inte längre är en möjlig metod för syftet samt att den bästa metoden för beräkning avantalet mobila enheter är en kombination av olika passiva Probe Request analyser.
Queiroz, Leonardo Mendonça Oliveira de. "Estimação e analise das perdas tecnicas na distribuição de energia eletrica". [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260679.
Texto completoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Este trabalho estuda a estimação de perdas técnicas na distribuição de energia elétrica e apresenta uma análise para a definição dos níveis adequados dessas perdas. Ambas as abordagens são focadas na regulação. É apresentada uma metodologia de estimação das perdas técnicas de energia baseada no valor médio e na variância dos pontos da curva de carga. Essa metodologia pode ser aplicada alternativamente aos métodos baseados na perda de potência máxima, que inserem imprecisões desnecessárias para a estimativa das perdas de energia. Foram desenvolvidos modelos de regressão para a estimação das perdas técnicas em redes de distribuição de média e baixa tensão, objetivando utilizar o mínimo de informações possíveis para uma precisão adequada. Uma metodologia de geração de redes foi desenvolvida para o estudo desses modelos, de forma a disponibilizar redes com características semelhantes às redes reais. Também são propostos aprimoramentos na estimativa das perdas em transformadores e ramais de ligação. Adicionalmente, é apresentada uma análise dos níveis adequados de perdas técnicas na distribuição, utilizando-se técnicas de engenharia e benchmarking. As propostas deste trabalho sugerem aprimoramentos na regulação das perdas técnicas, tornando o método de estimação das perdas mais preciso e introduzindo a análise de eficiência das distribuidoras.
Abstract: This work studies technical losses estimation in power distribution systems and analyses the adequacy of the losses. Both approaches are carried in a regulatory perspective. It is presented a methodology to estimate energy losses from the mean and the variance of the load curve points. This methodology can be applied in substitution of methods based on maximum power losses, which inserts unnecessary inaccuracy to the procedure. Regression models were developed to estimate technical losses in medium and low voltage distribution networks, aiming to require less information as possible to meet an appropriate accuracy. A methodology of networks generation was developed to make available networks with characteristics similar to the ones presented by real networks. Improvements in transformers and service conductors losses estimation were also proposed. Engineering and benchmarking techniques were applied to analyze technical losses adequacy. The proposals presented in this work may improve technical losses regulation, making the estimation of losses more accurate and introducing efficiency analysis of power distribution companies.
Doutorado
Automação
Doutor em Engenharia Elétrica
Radoš, Daniel. "Algoritmické obchodování na burze s využitím umělých neuronových sítí". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363869.
Texto completoHabtemariam, Mesay. "Bioinformatics Approach to Probe Protein-Protein Interactions: Understanding the Role of Interfacial Solvent in the Binding Sites of Protein-Protein Complexes;Network Based Predictions and Analysis of Human Proteins that Play Critical Roles in HIV Pathogenesis". VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/2997.
Texto completoSognestrand, Johanna y Matilda Österberg. "KOLLEKTIVTRAFIKENS GEOGRAFISKA VARIATIONER I TID OCH KOSTNAD – HUR PÅVERKAR DETTA BOSTADSPRISERNA? : Fallstudie Uppsala län med pendlingsomland". Thesis, University of Gävle, Ämnesavdelningen för samhällsbyggnad, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-5881.
Texto completoThe distance between home and work has increased in recent decades. By the development of infrastructure and public transport, jobs farther from home have become more accessible and this development has in turn increased commuting. Commuting travellers often pass over administrative boundaries which often serve as borders for public transport pricing. Also the market control prices. Research shows that travel times and costs significantly affect commuting choice. Many people have an upper limit of 60 minutes commuting distance between home and work. How commuting costs affect the individual's choice of commuting will vary depending on the individual's income and housing costs. The aim of our study was to see how public transport costs and travel times may vary geographically. GIS, Geographic Information System was used to make a network analysis which showed time distances and travel costs on maps. We also examined whether there was a link between towns accessibility by public transport and housing market which we did with help of correlation and regression analysis. In order to answer our questions we started from a study area consisting of Uppsala County with its surrounding commuting area. The maps showed how accessibility to larger towns varies among the smaller towns. The access is often best between bigger towns while there is less accessibility between smaller towns. The distance to bus stops or railway station also has a significant effect on how long the total travel time will be. Urban areas with access to rail services had the best opportunities to reach larger cities and that give also better access to labour market. From our study of the Uppsala County with a monocentric structure, we could indicate a link between accessibility to the bigger cities and housing prices in the surrounding towns. The higher commuting costs and longer travel time to the central place the lower the housing prices. A similar study of Stockholm which has a polycentric structure showed that the relationship between accessibility and house prices not are applicable to all regions. Here we can conclude that housing markets depends on many other factors than access to rapid public transport. House prices can depend on things like closeness to nature and water.
Avståndet mellan bostad och arbete har ökat under de senaste decennierna. Utvecklingen av infrastruktur och kollektivtrafik har lett till att arbetsplatser längre från hemmet har blivit mer tillgängliga och denna utveckling har i sin tur bidragit till en ökad arbetspendling i samhället. Pendlingsresenärer passerar ofta över administrativa gränser och dessa gränser styr ofta över kollektivtrafikens prissättning men även efterfrågan kan styra priset. Forskning visar att restider och kostnader i hög grad påverkar pendlingsvalet. Många människor föredrar ett pendlingsavstånd, mellan hem och arbete på högst 60 minuter. Hur pendlingskostnader påverkar individens val till pendling varierar bland annat beroende på individens inkomst och boendekostnader.
Syftet med vår studie var att se hur kollektivtrafikens kostnader och restider kan variera geografiskt. GIS, Geografiska Informationssystem, användes vid utförandet av en nätverks- och kostnadsanalys vilket visade tidsmässigt avstånd och kostnad på kartor. Vi undersökte också om det fanns ett samband mellan orters tillgänglighet med kollektivtrafik och bostadsmarknaden genom att utföra korrelations- och regressionsanalyser. För att svara på våra frågeställningar utgick vi från ett undersökningsområde bestående av Uppsala län med pendlingsomland.
Kartbilderna visade tydligt hur tillgängligheten till större städer varierar mellan olika orter och att tillgängligheten ofta är bäst mellan större tätorter medan det är sämre tillgänglighet mellan mindre tätorter. Avståndet till hållplatser har också betydande påverkan på hur lång den totala restiden blir. Tätorter med tillgång till järnvägstrafik hade det bästa möjligheterna att nå större tätorter och därmed blir arbetsmarknaden större för dessa orter. Från vår studie över Uppsala län som kan anses ha monocentrisk struktur kunde vi även tyda ett samband mellan tätorters tillgänglighet till centralorten och orternas bostadspriser. Ju högre pendlingskostnad och längre restid till centralorten desto lägre var orternas bostadspriser. En likadan studie över Stockholm som har en mer polycentrisk struktur visade dock att detta samband mellan tillgänglighet och bostadspriser inte gäller för alla regioner. Här kan vi dra den slutsatsen att bostadsmarknaden styrs av många andra faktorer än tillgång till snabb kollektivtrafik och att vissa områdens bostadspriser mer styrs av exempelvis närhet till natur och vatten.
Chasák, Petr. "Analýza informačního systému firmy a návrh změn". Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2010. http://www.nusl.cz/ntk/nusl-222630.
Texto completoChloupek, Pavel. "Posouzení informačního systému firmy a návrh změn". Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2011. http://www.nusl.cz/ntk/nusl-222836.
Texto completoŠulc, Martin. "Návrh marketingového plánu pro firmu specializovanou na informační zboží". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236393.
Texto completoMaršová, Eliška. "Predikce hodnot v čase". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255333.
Texto completoTupý, Maroš. "Rizika v oblasti řízení marketingových nástrojů". Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-382727.
Texto completoCholachue, Ngounou Christel. "Caractérisation des blindages électromagnétiques des câbles et faisceaux aéronautiques". Thesis, Normandie, 2020. http://www.theses.fr/2020NORMR025.
Texto completoDuring the last decade, the proliferation of on-board leisure activities in the new aircrafts have been growing exponentially. In the airplane like A380, each seat integrates several functions (video games, music, etc. ..) Additionally, each function must be connected using at least one electric cable. This system requires a significant number of kilometers of cables to establish all the on-board electrical connections. Furthermore, for reasons of safety and security related to mechanical, hydraulic or pneumatic functions, the wiring EMC requirements associated to the massive progressive electrification becomes considerably stricter. The coexistence of kilometer lengths of cables system in such a small space has increased the requirements in terms of electromagnetic (EM) shielding. Most of existing numerous methods for analyzing the shielding of cables and harnesses are limited in terms of computation speed, design process and in accuracies for the multiport systems analysis. Moreover, most of popular simulation and commercial tools are very expensive (for example with license cost can be more than 18K€). The use of commercial tools requires advanced skills and a lot of time to characterize the shielding of cables and harnesses. For example, with a simulation tool like HFSSR from ANSYSR , the computation time may cost approximately 3 hours to create a design model of a braided shields heath. Then, the computed results can be generated during an average simulation time of 20 minutes using a PC equipped with an Intel single-core processor RXeon RCPU E5-1620 v4 @ 3.50 GHz and 32 GB of physical RAM with 64-bit Windows 10. Most of available methods and techniques for characterizing the shielding of aeronautical cables and cable harnesses have shown their limits. For example, most of existing triaxial benches are particularly difficult to deploy for the transfer impedance measurements and they cannot operate beyond 100 MHz. The present PhD thesis aims to overcome these technical limits. Doing this, an original analytical method is developed for extracting S-parameters from multiport systems under fast computation speed and design process. An innovative methodology of EMC modelling was proposed. The knowledge of S-parameters is helpful to determine the broadband EM intrinsic parameters of the cabling as coaxial system. The developed analytical and semi-hybrid model is based on the unfamiliar formalism using tensorial analysis of networks (TAN) based on the Kron’s method. The model offers an outstanding possibility to analyze complex systems with deep knowledge of physical phenomenal behind the EM shielding. Thanks to the TAN formalism, an innovative method of circuit theory has been developed to determine the shielding efficiency (SE) of shielded cable. The feasibility of this multiport S-parameter approach was verified with the consideration of EM coupling between a nude cable constituting an internal conductor and a braided cable placed in parallel. More importantly, an advanced study of shielding efficiency (SE) with respect to the EM coupling configuration between a shielded coaxial cable and a loop probe is performed. Substantially, it was noteworthy that the TAN formalism provides an illuminating know-how on the theoretical, numerical and experimental analyses of cables and bundles EM shielding, and transfer impedances of the shielding sheath. Moreover, the TAN modelling effectiveness was confirmed with different applications with computation time which does not exceed milliseconds. Finally, the TAN model was also used to develop a SE characterization bench for tubular EM shielding structures up to 300 MHz. Emphatically, broadband SE and transfer impedance results in good correlation between 3D simulations and measurements were obtained
Silva, Ana Catarina Lopes da. "Pricing network analysis of a supermarket chain in Portugal". Master's thesis, 2022. http://hdl.handle.net/10362/133847.
Texto completoWith the growth of digital transformation, the business is now conducted in the digital age, opening new markets and new business opportunities with a simple click, sitting at home. Along with it, as the internet grows, more data is exposed, which leads us to competitive advantages, especially during the pandemic and in the post-pandemic period. This work explores daily price data from the online website of one of the largest supermarket chains in Portugal, covering the period between February 2020 to March of 2021. To observe the market and the competition behavior, we applied the science of networks, a data mining technique that provides deeper insight into the structure of the retailer market. We used the Maximum Spanning Tree and added the most relevant links from the correlation matrix to represent the retail market. As a starting point, we were able to build five networks that represent the daily price changes for products, categories, and brands. Moreover, we study the network interactions using centrality measures, namely: degree, betweenness, closeness, and eigenvector. These outputs were useful by creating better visibility of the market using real data from online retailing platforms and making available the first input for future work and further research to improve the pricing strategies.
Yarish, David. "Utilizing Crowd Sourced Analytics for Building Smarter Mobile Infrastructure and Achieving Better Quality of Experience". Thesis, 2015. http://hdl.handle.net/1828/7000.
Texto completoGraduate
davidyarish@gmail.com
LIU, CHUN-YU y 劉峻宇. "Applying Technical Analysis and Neural Networks for Stock Price Prediction". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/st87nz.
Texto completo國立臺北大學
電機工程學系
106
The stock market forecasting is an important issue in financial engineering. An accurate forecasting system helps investors obtain high profit margin. With the development of technologies and the evolution of big data, the stock market investment will no longer be directly performed by the human; instead, intelligent investment will provide investors more accurate strategy analysis and more effective investment decisions. Therefore, this study proposed to combine the technical analysis pointers with the back propagation neural network. The technical analysis provided several useful functions such as stock price analysis, forecasting, and obtaining the key data in the stock price. We used the technical pointers instead of the raw data as the input variables of neural networks and verified if the pre-processing data can achieve more accurate stock price prediction. The technical analysis indicator package was written in the R language. The four major indexes of U.S stock market, Dow Jones Industrial Average, Philadelphia Semiconductor Index, Standard & Poor's 500 Index, NASDAQ Composite Index and sixteen listed companies serve as the sample data. Several kinds of pre-processing models were introduced. Through looking into the experimental results, the proposed package helped the neural networks achieve better performance. The proposed package passed a comprehensive R archive network (CRAN) check and made contribution to R in the field of stock data analysis.
"Application of neural network to study share price volatility". 1999. http://library.cuhk.edu.hk/record=b5896263.
Texto completoThesis (M.B.A.)--Chinese University of Hong Kong, 1999.
Includes bibliographical references (leaves 72-73).
ABSTRACT --- p.ii.
TABLE OF CONTENTS --- p.iv.
Section
Chapter I. --- OBJECTIVE --- p.1
Chapter II. --- INTRODUCTION --- p.3
The principal investment risk --- p.3
Effect of risk on investment --- p.4
Investors' concern for investment risk --- p.6
Chapter III. --- THE INPUT PARAMETERS --- p.9
Chapter IV. --- LITERATURE REVIEW --- p.15
What is an artificial neural network? --- p.15
What is a neuron? --- p.16
Biological versus artificial neuron --- p.16
Operation of a neural network --- p.17
Neural network paradigm --- p.20
Feedforward as the most suitable form of neural network --- p.22
Capability of neural network --- p.23
The learning process --- p.25
Testing the network --- p.29
Neural network computing --- p.29
Neural network versus conventional computer --- p.30
Neural network versus a knowledge based system --- p.32
Strength of neural network --- p.34
Weaknesses of neural network --- p.35
Chapter V. --- NEURAL NETWORK AS A TOOL FOR INVESTMENT ANALYSIS --- p.38
Neural network in financial applications --- p.38
Trading in the stock market --- p.41
Why neural network could outperform in the stock market? --- p.43
Applications of neural network --- p.45
Chapter VI. --- BUILDING THE NEURAL NETWORK MODEL --- p.47
Implementation process --- p.48
Step 1´ؤ Problem specification --- p.49
Step 2 ´ؤ Data collection --- p.51
Step 3 ´ؤ Data analysis and transformation --- p.55
Step 4 ´ؤ Training data set extraction --- p.58
Step 5 ´ؤ Selection of network architecture --- p.60
Step 6 ´ؤ Selection of training algorithm --- p.62
Step 7 ´ؤ Training the network --- p.64
Step 8 ´ؤ Model deployment --- p.65
Chapter 7 --- RESULT AND CONCLUSION --- p.67
Result --- p.67
Conclusion --- p.69
BIBLIOGRAPHY --- p.72
Wang, Kung-Piao y 王光麃. "The Analysis of Monopoly’s Optimal Quality and Price Under Network Externality Surroundings". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/99708225443111255093.
Texto completo國立高雄第一科技大學
運輸倉儲營運所
91
Katz and Shapiro (1985) deemed that network externality originated from increment of product consumption which emanates more demand quantity. It is possible that network is from network externality and both of them belong to positive relation, meaning that increment of consumption can raise network externality. Based on classical quality demand theory, quality is viewed as exogenous variable .In the other hand, this study manipulate quality as a ingenuous variable and therefore, when pricing optimally, firms need to think about positive and negative effects caused by quality at the same time. This study employs monopoly and duopoly models to analyses firm’s optimal price and quality in comparative static analysis. Results reveal that in duopoly model, one firm is located on 1/3 and the other is located on .2/3, which is distributed averagely in the market. According to monopoly structure, equilibrium price and quantity are under increasing condition and in addition, quality demand elasticity and price demand elasticity display decreasing phenomenon. Ultimately, monopoly model is revised to dynamic optimal control theory with one state variable (reputation) ,one control variable (quality).and space economic idea proposed by Hotelling In dynamic control policy, monopoly structure lying in autonomous system is under saddle point equilibrium. In the non-autonomous system, quality and reputation reveals a stable equilibrium.
Li, Kuo-Cheng y 李國成. "Prediction on Stock Price Variations Using Keyword Analysis and Recurrent Neural Network". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/yxt87r.
Texto completo國立臺灣科技大學
資訊管理系
106
Nowadays, the stock is still the most popular investment tool in our society. To find the time of trading for a stock is the most important issue that concerns all the investors. When selecting an investment target and finding the time of trading, most of investors take advantage of technical analysis and fundamental analysis. However, the influence of financial news on the stock market should also be taken into account since the frequently released financial news carry large amount of information that influences the expectation of an investor on a stock. As such, the financial news may affect the trading behavior of the investor. This thesis aims at constructing a prediction model on the variations of a stock price based on the company-related news and technical analysis of the stock. To this end, we collected financial news of several listed stocks in Taiwan Stock Exchange (TWSE). We first use text mining algorithm to filter out important keywords from the news and calculate their corresponding scores of importance. Then, we train to find the representing vectors of the important words using Word2Vec model. Finally, the vectors of important words of the daily news of a stock are multiplied by their corresponding scores of importance to generate a news vector for the stock. With the daily news vector and several financial variables, we construct a prediction model on the selected stocks using the long short-term memory (LSTM) recurrent neural network. We conducted several experiments on the proposed model, termed Text-LSTM, a model without considering the daily news, termed LSTM, and a model constructed using ARIMA model. The experimental results showed that the Text-LSTM achieved an average of 61.8% accuracy and a maximum of 69.3% accuracy in Directional Symmetry (DS) on the predictions. The accuracy of the Text-LSTM outperforms those of the pure LSTM and the ARIMA by 6.2% and 13.4%, respectively.
Yu-DeLin y 林宇德. "Text Analysis for Prediction of Bitcoin Price by Sequence Neural Network Model". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/r8hw6e.
Texto completo國立成功大學
資訊工程學系
106
With the accelerated development of artificial intelligence, some people want to use it to predict market trends. Simultaneously, digital currency, headed by Bitcoin and Ethereum, caught people’s attention because of its soaring price in last year. The reputation of digital currency get higher and higher in social media and traditional media. People certainly hope to use AI to predict the digital currency market. In this research, we use Twitter posts as training data and vectored method to represent the tweet information (day vector) per day. After cleaning Twitter raw data, we converted the tweets in the giving day as day vector and feed the day vector to sequence to Sequence model use to predict the change of Bitcoin price. The entire system uses attention model in day vector model and the sequence to sequence model, respectively. The experiments show that the prediction accuracy rise slightly by increasing day vector dimension and the attention model of the SequenceDecoder model can significantly improve the accuracy. Finally, we analyzed the 7-day predicted results individually and found that the accuracy decrease when predicting latter day. This meet our understanding that it is harder to predict the latter day than the near day.
Gullapalli, Sneha. "Learning to predict cryptocurrency price using artificial neural network models of time series". 2018. http://hdl.handle.net/2097/38867.
Texto completoDepartment of Computer Science
William H. Hsu
Cryptocurrencies are digital currencies that have garnered significant investor attention in the financial markets. The aim of this project is to predict the daily price, particularly the daily high and closing price, of the cryptocurrency Bitcoin. This plays a vital role in making trading decisions. There exist various factors which affect the price of Bitcoin, thereby making price prediction a complex and technically challenging task. To perform prediction, we trained temporal neural networks such as time-delay neural networks (TDNN) and recurrent neural networks (RNN) on historical time series – that is, past prices of Bitcoin over several years. Features such as the opening price, highest price, lowest price, closing price, and volume of a currency over several preceding quarters were taken into consideration so as to predict the highest and closing price of the next day. We designed and implemented TDNNs and RNNs using the NeuroSolutions artificial neural network (ANN) development environment to build predictive models and evaluated them by computing various measures such as the MSE (mean square error), NMSE (normalized mean square error), and r (Pearson’s correlation coefficient) on a continuation of the training data from each time series, held out for validation.
莊文慶. "The Analysis of Stock Price from Macro-Economic Factors : -- Apply Artificial Neural Network". Thesis, 2001. http://ndltd.ncl.edu.tw/handle/57337091607566322842.
Texto completoHUNG, YUNG-YI y 洪詠譯. "Jet Fuel Price Forecasting with Time Series Analysis and Artificial Neural Networks". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/88449903669522035173.
Texto completo國立高雄第一科技大學
運籌管理所
97
Abstract This study forecasts jet fuel price with time series analysis and artificial neural networks. The fuel price of aviation fluctuates violently, the fuel cost accounts for more than 50% of airline''s total cost at the time of the peak, cause the enormous business losses. At the time of fast drop, because of fuel hedge direction mistake, it has to withdraw huge amortization of non-operating loss also. So airlines who can make a profit or suffer the loss continuously depend on its fuel supply chain stability and success of fuel hedge definitely. Hence, predict the tendency of the jet fuel price, not only the focal point of reducing expenditure, but also could obtain non-operating income. As to fuel hedge operations and energy supply chain stability, the prediction ability of the future price is important keys. So this research combines some time series models, for instance: ARIMA, GARCH, VAR, VECM and structural change point method, with artificial neural networks (ANN) to forecast the Singapore jet fuel spot price. This Research looks for oil price volatile factors and historical datum with relevant documents at first, and then selects proper parameters and prediction models of time series analysis and artificial neural networks. With EIA Petroleum (2009) data from June of 1986 to November of 2008 , in empirical research, finally succeed in to gain three models of the good average accuracy of out sample prediction, are respectively to 89.83%, 91.87% and 95.85%. So, these models of this research could be offered to the airlines as reference on forecasting decisions of the jet fuel price.
WEN-HWA, CHEN y 陳文華. "THE SENSITIVITY ANALYSIS FOR VARIABLES OF FORECASTING GOVERNMENT BOND PRICE VIA USING NEURAL NETWORK". Thesis, 2001. http://ndltd.ncl.edu.tw/handle/05018225584382357818.
Texto completo國立臺灣大學
國際企業學研究所
89
This research aims to explore (1)the important variables for forecasting government bond price and the leading periods in which those variables influence government bond price. (2)whether the relative importance of every variables would change with time.(3)to fine tune the input variables for neural network and to examine the improvement of prediction performance ,according to the importance of variables in every interval. The key characteristics of this research methodology include (1)using unit root test for input variables in order to avoid spurious regression. (2)using adjusted Granger causality test to screen input variables and simultaneously to find out the leading periods of every variables. (3)dividing the variables into major and minor variables. If the major variables couldn’t be selected, they would be kept as the input variables to avoid the pitfall of the linear model for unpurposely screening out the important un-linear related variables. (4)designing the dynamic Sensitivity Analysis and Granger Causality test and illustrating the time track of the importance of every input variables. (5)constructing the neural network by adjusting dynamically the set of input variables. This study contributes to both academic and application researching in the following four aspects.(1)when using neural network for forecasting, latest variables should not be directly input but to consider the periods the input variables lead output variables. (2)whether the importance of variables will change in different interval should be considered. If the importance of every variable changes with time, the same variables should not be input for a long time during forecasting.(3)Improving the prediction performance by capturing the change of the importance of the input variables. (4)describing every variables in influencing government bond price (ex. positive or negative relation between them or mainly long term influence or mainly short term influence)
OU, YAO-LUN y 歐曜綸. "Integration of Artificial Neural Network and Technical Analysis for Stock Price Prediction in Taiwan". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/t5844d.
Texto completo中國文化大學
財務金融學系
107
Following the development of artificial intelligence, practitioners are gradually using artificial intelligence to handle complex huge amounts of data (big data). This study uses a neural network that is also a kind of artificial intelligence technology and combines with technical analysis data as the predictive variables of artificial neural network. First, we compare the predicted performance of different hidden layer and different neuron number combinations, and then compare the performance of GARCH volatility as the predictive variable of artificial neural network. We use the historical share price data of MSCI Taiwan Index for the period of 2014-2018 and view the predicted performance through historical share price data. The artificial neural network with excellent fault tolerance, even if there is noise data also can produce more accurate prediction results. Many previous studies and literature used artificial neural networks as a research tool to improve predictive performance. This study can be used as a reference basis for the prediction of the price, volatility, etc. by using artificial neural networks. In particularly, the technical analysis of data as a variable for price forecasting research.
Batsukh, Tushigmaa y 圖史格. "Chaos for Rare Earth Elements Price forecasting-An Analysis of Artificial Neural Networks". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/56124205566294384217.
Texto completo中原大學
企業管理研究所
100
This study aims to predict the price of the Rare Earth Elements (REE), which are key components of green energy technologies and other high technology applications. The use of REEs in modern technology has increased dramatically over the past years. Based on the growing global demand for REEs and the limited supply, there is growing concern that the world may soon face a shortage of the materials. As a result, prices have risen significantly. A rise in prices in REEs market creates challenging for mining and manufacturing companies in pricing of products and services because of high cost raw material. To reduce REE explosion risk and make hedging, future investment and evaluation decision will depend on accurately forecasting future price trend. This study applied three different approaches, Brock-Dechert Scheinkman test, Rescaled range analysis and Correlation Dimension Analysis for detection of chaotic phenomena. Next this study utilizes the artificial neural networks, including back propagation network (BPN) and Time delay recurrent neural network (TDRNN) to make a prediction for the price of REEs associated with the inputs such as The Broad Index, Baltic Dry Index, Commodity Research Bureau Futures Price Index, PHLX Semiconductor Sector index, NASDAQ Computer Index and The London Interbank Offered Rate. The result will explore the price behavior behind the REEs and to provide investors by the valuable information, this study compares which network forecast is more accurate. The simulation resulted that the chaos effect is exists in REE prices and suggest that employing TDRNN in price data of REE is more effective than BPN and the best performance is attained by the TDRNN. Therefore, REEs associated inputs, The Broad Index, Baltic Dry Index, Commodity Research Bureau Futures Price Index, PHLX Semiconductor Sector index, NASDAQ Computer Index and The London Interbank Offered Rate would be good indicator for forecasting REEs.
Liao, Wen-Jung y 廖文榮. "A neural network system implementation of stocks price forecasting model based on MA analysis technique". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/87159872231585678404.
Texto completo國立中興大學
電子商務研究所
96
This paper develops a model of a trading system by using neuro-fuzzy framework in order to better predict the stock market index. This research is proposed to integrate every component in the stock analysis system. For this reason, the analysis system is a flexible and adjustable through calling by each component. Dynamic parameters can be automatically analyzed the model developed in the research. The empirical results show strong evidence of nonlinearity in the stock market index by using moving average based technical trading rules. Theoretically our approach shows that the neuro-fuzzy model may allow investors to earn higher returns in the dynamic model when profit making opportunities exist and can be exploited with an efficient information processing and learning system strategically.
Cheng, Chi-Wei y 鄭志偉. "A Neural Network Approach for Prediction and Analysis in the Taiwan Stock Market --- Stock Price and TSEWPI". Thesis, 1997. http://ndltd.ncl.edu.tw/handle/84882368526384217481.
Texto completo淡江大學
資訊管理學系
85
This study attempts to select the significant ones from various offinancial ratios and indicators about sale events from the viewpoint offundamental analysis . After interviewing with investment experts and referencing the results from related studies , some indicators withcorrelation were eliminated and a group of indicators was reserved . Back-Propagation Network (BPN) approach is used in this study. We collectretrospective data as BPN''s training samples and testing samples from four listed companies that are with similar industrial architectures . To prevent the noise from the effects of whole market and sector (consists of companies which have similar products) , we proposed amodule which is used to predict the relative trends between individual stock price and sector it belongs . Results indicate that the hit ratiosare about 59% in the individual stock module and about 67% in the sector-relative module on the end of the first month in which companiesopen their accounting information , and there are no obvious predictingcapacities after the second and the third month . This research also present a model that discusses the mutualrelations among GNP growth rate , interest rate and weighted EPR(Earning-Price Ratio) of whole listed companies on Taiwan Stock Exchange Company .We adopted neural network approach to identifying the forecasting - capacities of this model on long-term fluctuation of TSEWPI . The modelwe have proposed use retrospective date to discuss the relations betweenGNP growth rate and aggregate profits of whole listed company , the cyclic phenomenon within interest rate and PER of listed company , andtime-lagging effect . We hope this model can be applied on real world like TSEWPI trend prediction , for example , forecasting the peak and trough of TSEWPI curve .
Wang, Hwa-Yee y 王華頤. "The Analysis of The Financial Volatility Against Stock Price Based on Neural Network Under Client-Sever Infrastructure". Thesis, 1997. http://ndltd.ncl.edu.tw/handle/46694516894705557155.
Texto completo國立交通大學
資訊管理研究所
85
To the investors and speculators in the uncertain stock market, the most they desire to know is the analysis point of stock volatility. However, the volatility is critically related to profits and strategies of companies they invested. Therefore, if we can further hold and make use of the financial reports of individual industry to predict the future operation performance which will become the basis of comparison and analysis.This research is based on the neural network to control the behavior mode of volatility rate under important financial index, and use the volatility rate of financial index as a factor to verify if the volatility is ahead of the financial reports announced.At the same time, the research is also from the viewpoint of system integration to combine the database with the outcome of behavior mode described as above and apply to the internet under the modified infrastructure of client-sever database and the World- Wide-Web for the public to operate this analysis system and access what they need.We attribute two consequences after research 1、By the way of financial volatility rate this month to learn the stock volatility rate this month, the hit rate is the highest in predicting stock volatility. However, on the other side, we get the lowest hit rate by this month to predict next month. This result shows there is not much relativity between share price and financial information after financial reports announced. In other words, share prices have fluctuated before financial reports announced.2、For the complex and fast- updating environment under the client-server database system, we use the communicators and publishers and subscribers to modify the client-server infrastructure, not only to reduce the load of core database, but also enhance the performance of system, moreover, we can provide the more convenient and easier way for system conversion and data exchange.
Jou, Fa y 周發. "The Study of Real Estate Price Indicator in Northern Taiwan-An Analysis of Chaos and Artificial Neural Network". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/81144995681851080557.
Texto completo中原大學
企業管理研究所
100
Real estate is the one of the investment targets for public favorite in Taiwan. Land as wealth was relatively important for investors. And the real estate not only provided demand of living, but also revealed a symbol of wealth and status. The house prices changed caused by the rarity of land. Demand and supply reflecting the the cyclical trend are two important interactive factors of the real estate market to make choose. Through the analysis results provided the appropriate investment opportunities and enhanced investing profit for self-occupied or investment occupied purchases. This study utilizes BDS test, R/S analysis, and Correlation Dimension Analysis to examine whether Leading Indicators, Coincident Indicators, Cathay Home Price Index, and the Lutheran Home Price Index have the chaos phenomenon. This paper uses a suitable nonlinear prediction of Back-Propagation Neural Network (BPNN) and Time-Delay Recurrent Neural Network (TDRNN), joining ten independent variables (such as Number of the country's population growth, economic growth rate, construction stocks index, gross domestic product, consumer price index, home mortgage rates, the license of construction permits, land value-added tax, and the money supply) and compares the predict performance of the models. The samples are divided into 40 groups. The period was from first quarter of 2001 to the fourth quarter of 2011. The results of BDS test and R/S analysis showed that are significant, and the outputs of Correlation Dimension Analysis are convergence. This indicated that the Leading Indicators, Coincident Indicators, Cathay Home Price Index, and the Lutheran Home Price Index have the chaos phenomenon, suggesting that the real estate price was predictability. The results of neural network indicated that population growth, economic growth rate, construction stocks index, gross domestic product, consumer price index, home mortgage rates, the license of construction permits, land value-added tax, and the money supply were suitable for real estate price forecasting. The Back-Propagation Neural Network (BPNN) processed the predict ability to have the better performance for Leading indicators, Coincident Indicators, Cathay Home Price Index and the Lutheran Home Price Index . In comparison of the results for national level of Leading Indicators, Coincident Indicators, Cathay House Price Index and the Lutheran Home Price Index, and Taipei, new Taipei city, Taoyuan County of the Cathay House Price Index with regional level for the Lutheran Home Price Index samples, this paper found that the Coincident indicators have better forecast performance. In comparison regional prices forecasting, Taipei is more suitable for detecting the prices trend.
Hubáček, Lukáš. "LinkedIn jako fenomén na trhu práce". Master's thesis, 2016. http://www.nusl.cz/ntk/nusl-266283.
Texto completoBubeníčková, Veronika. "Sítě zájmových skupin - zájmové skupiny, jejich pozice a vztahy v legislativním procesu EU". Master's thesis, 2014. http://www.nusl.cz/ntk/nusl-330494.
Texto completoHsu, Pei-chu y 徐培哲. "Using regression analysis , Kalman filter , and neural networks to the prediction of tendering price on roadway construction". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/10126315070326085395.
Texto completo逢甲大學
土木工程所
95
Although domestic public infrastructure budget is notified during tendering (internet notice in accordance with Article 27 of Procurement Act), bottom price used to select vendor is setup by the proprietor. In addition, due to fierce market competitions, most vendors make deductions based on estimated price during tendering to increase the likelihood of winning a tender. Based on existing public engineering tender procedures in this country, most infrastructure project vendors are selected using the lowest bid mechanism. However, this type of selection method is likely to result to inappropriate tender strategies. Excessive bidding result to vicious price cut among bidders. It will in turn affect the quality of tender. Therefore, this study has constructed more precise engineering bidding price prediction model based on related mathematical statistics basics to assist vendors increase the likelihood of winning bid and optimize resource distribution and application. This study adopts engineering tenders of road infrastructure sponsor in 2005 as study subjects. Public invitation for tender in the amount of NT$50million or lower and NT$1million or higher (not exceeding threshold amount value) serves as the engineering tender sample. Based on engineering tender related data such as bottom price, price of award, budget price, contract period (calendar day), and bid bond, lowest price of award and proprietor ceiling price prediction model is constructed using Regression Analysis, Kalman Filter, and Artificial Neural Networks. In consideration of regional influential factors, tender data source is divided as to region into northern region, central region, southern region, eastern region, and integrated. Results of comparative analysis conducted show that the error range for these three prediction methods is between 10.63% and 24.29%. Meanwhile, the respective prediction methods do not show absolute systematic advantage or disadvantage. Moreover, prediction on bottom price is more feasible. As for prediction for price of award, since more influential factors are involved and there are more unpredictable decision-making behaviors during tendering, it is impossible to construct a set of more reasonable quantified mathematical prediction model.
Lin, Chun-hui y 林春暉. "Application of Gray Relational Analysis Method and Neural Networks on The Real Estate Investment Trust Price Forecasting". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/92796657772033188704.
Texto completo義守大學
工業管理學系
102
In recent years, Real Estate Investment Trust (REIT) has become a popular investment around the world. REIT has the advantages of inflation-resistance as well as real estate and high return on stock market. Researchers of Taiwan institute Economic Research expect that the situation of investment in real estate will become worse in the second half of 2013 and the Burst of Real Estate Bubbles could happen around 2015. Therefore, by considering how to reduce the risk of investing real estate and anticipating return rate correctly, those techniques not only can help the investors choose their investment but also keep them from the effect of the Burst of Real Estate Bubbles. This research combines the factors of acroeconomics and the performance of investing real estate in the past focuses on real estate in Taiwan by using Grey Relational Analysis and Artificial Neural Network. Therefore, the main purpose of prediction is to estimate the oncoming events or situations in advance and provide the best information to management level to detect those uncertain conditions and help reduce the risks during decision-making process. After that, apply the result on production output assessment to increase the accuracy of forecast and provide the consultation to related industries for production forecasting.
Chen, Yi-En y 陳以恩. "Trend Analysis of House Pricing using Neural Network with Adaptive Differential Evolution - A Case Study of Taiwanese Real Price Registration". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/fmkvfs.
Texto completo國立交通大學
資訊管理研究所
104
Real estate market has its own method for pricing depends on each property’s location、type and characteristics etc. It makes the difficulty for predict the trend of real estate market. In recent years, the Taiwan Government started to promote the Real Price Registration System to record all data of real estate transaction publicly. This RPR system is used to intensify the price transparency of real estate market. With higher transparency, the prediction model could be made more effective and reliable. In this research, we would like to build up a prediction model of house pricing which is made by RPR transaction data. We focus on the properties which are domestic estate in Taipei City and were traded from August 2014 to February 2015. Using the features, for example, address、type of building、floor made a K-Means clustering model to recognize different groups of buyers and sub-market. The prediction model is made for each sub-market by using modified neural network and time series methods.