Relevant bibliographies by topics / Stock analysis software

Contents

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

Academic literature on the topic 'Stock analysis software'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Stock analysis software"

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HSM, Zani Anjani Rafsanjani. "ANALISA LAJU PERUBAHAN HARGA SAHAM LQ45 MENGGUNAKAN PERSAMAAN DIFERENSIAL." Jurnal Riset Akuntansi Politala 3, no. 2 (December 29, 2020): 60. http://dx.doi.org/10.34128/jra.v3i2.68.

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The stock price movement is a very interesting discussion today. Dynamic price changes every time requires deep analysis to determine trends and stock price predictions in the future. There have been many methods used to analyze and predict stock prices. This paper will analyze the acceleration of stock price changes using a mathematical approach, known as a second-order differential equation. The benefit of this research is to obtain a coefficient of change in stock prices that can be used to predict stock prices in the future. Stock prices that will be observed are stocks including the LQ45 category. Furthermore, program analysis is carried out using Matlab software. At the end of the study, the coefficient of price change for LQ45 stocks was generated through provided historical data.
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Zhu, Rong, Zuo Quan Zhang, Xiao Yue Li, Xuan Wu, and Su Zhang. "The Study on the Plasticity Theoretical Models of the Volatility of Stock Prices." Advanced Materials Research 518-523 (May 2012): 5963–67. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.5963.

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This paper analyzes the characteristics of the stock price fluctuation compared with elastic-plastic theory in mechanics and introduces the concept of stock equilibrium price, plasticity of stock price analogically. A basic model of the stock plasticity under the relationship between stock price fluctuation and trading volume changes is also built. Tested by 20 kinds of stocks from Shanghai and Shenzhen stock markets in China by using the econometric analysis software Eviews3.0 afterwards, the basic model is improved, and three developed models are built from it. Finally, this paper obtains more scientific and reasonable stock price plasticity model after the comparative analysis of the four previous models.
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Peng, Xia, and Ping Zhu. "Requirement Analysis of Storage Management System." Applied Mechanics and Materials 263-266 (December 2012): 1438–41. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1438.

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The storage management system manages and controls for storage in-out stock activities, and has an important significance for coordinating production and operation of enterprise, improving economic efficiency. Requirement analysis is the basis of software development, mainly is functional requirement analysis and performance requirement analysis. First, do the functional requirement analysis, through analysis the business processes, analysis the main functions and processes of system with data flow diagram, and list the buyer, purchase commodity information, sales production, commodity stock table and data dictionary item. Then, do performance requirement analysis; describe the main content of the three aspects of accuracy, time characteristics and flexibility, especially analysis the C/S structure model to build software systems. In this paper, describes the overall concept of software function and performance as the specific software requirement specification, lays a foundation for the development of storage management system.
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Barberà-Mariné, M. Glòria, Yanina Laumann, and Laura Fabregat-Aibar. "Analysis of Fuzzy Beta Coefficients. Evidence from the Mexican Stock Market." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, Suppl. 1 (December 2018): 59–69. http://dx.doi.org/10.1142/s0218488518400044.

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This paper represents a contribution to the empirical literature on systematic risk at a sectoral level in an emerging market, the Mexican Stock Market, incorporating all the available information of the different asset quoted prices for the beta calculation. We estimate the fuzzy beta coefficients for individual stocks as well as for sectoral indices comparing the results with OLS beta coefficients. Then, we contrast if the fuzzy estimations verify two hypothesis of the traditional portfolio theory, specifically those related to the influence of the number of stocks and the length of estimation period over beta stability. The methodology used to calculate the fuzzy beta coefficients is the fuzzy linear regression. The results suggest that, in the Mexican Stock Market, hypotheses of the portfolio theory are also verified when the return of the portfolio is considered as an uncertain data.
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Dařena, František, Jonáš Petrovský, Jan Žižka, and Jan Přichystal. "Machine Learning-Based Analysis of the Association Between Online Texts and Stock Price Movements." Inteligencia Artificial 21, no. 61 (May 9, 2018): 95. http://dx.doi.org/10.4114/intartif.vol21iss61pp95-110.

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The paper presents the result of experiments that were designed with the goal of revealing the association between texts published in online environments (Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies at a micro level. The association between lexicon detected sentiment and stock price movements was not confirmed. It was, however, possible to reveal and quantify such association with the application of machine learning-based classification. From the experiments it was obvious that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, lags between the release of documents and related stock price changes, five levels of a minimal stock price change, three different weighting schemes for structured document representation, and six classifiers were studied. It has been shown that at least part of the movement of stock prices is associated with the textual content if a proper combination of processing parameters is selected.
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Li, Xiaodong, Haoran Xie, Ran Wang, Yi Cai, Jingjing Cao, Feng Wang, Huaqing Min, and Xiaotie Deng. "Empirical analysis: stock market prediction via extreme learning machine." Neural Computing and Applications 27, no. 1 (February 2, 2014): 67–78. http://dx.doi.org/10.1007/s00521-014-1550-z.

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Hayat, Nur Mishbah, Agung Budi Prasetijo, and Risma Septiana. "Analisis Kinerja Algoritma J48 Decision Tree untuk Pengambilan Keputusan Beli/Jual pada Saham PT Harum Energi Tbk. (HRUM)." JTIM : Jurnal Teknologi Informasi dan Multimedia 1, no. 3 (November 7, 2019): 244–53. http://dx.doi.org/10.35746/jtim.v1i3.43.

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The problem that is often faced by investors in selling / buying stocks is the difficulty in analyzing a dataset of stock prices in large quantities.This analysis aims to predict the rise or fall of stock prices based on data obtained. To assist investors in determining buying / selling decisions on stock analysis based on technical and equipped with classification techniques in data mining. This study analyzes the performance of the J48 Decision Tree algorithm in the Waikato Environmental Software for Knowledge Analysis (WEKA) version 3.8.2 for PT. Harum Energi Tbk. (HRUM). The results showed in the testing data, the percentage of testing on data without normalization was higher by 87.3 (non-aggressive) and 88.8 (aggressive) compared to normalized data 84.2 (non-aggressive) and 85 (aggressive ). The biggest stock profit generated is in non-aggressive type data without normalized by 48.75 or Rp. 48,750.00.
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Shen, Kao-Yi, and Gwo-Hshiung Tzeng. "Combined soft computing model for value stock selection based on fundamental analysis." Applied Soft Computing 37 (December 2015): 142–55. http://dx.doi.org/10.1016/j.asoc.2015.07.030.

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Li, Hua, and Hong Zhou. "An Empirical Analysis on the Influence Factors of Stock Market." Advanced Materials Research 403-408 (November 2011): 353–57. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.353.

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Many scholars have studied the influence of the single variable to the stock market. In this paper, regression analysis method and Econometrics software Eviews 6.0 are used to solve the problem. Through analyzing the inner relationship among stock trading volume, money supply, interbank lending rates, and consumer confidence index, this paper attempts to establish a multiple regression equation and eliminate the problems of multicollinearity, autocorrelation and heteroscedasticity at the same time. The empirical analysis proves the model has certain rationality which can provide the basis for investors on stock investment.
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Jin, Zhigang, Yang Yang, and Yuhong Liu. "Stock closing price prediction based on sentiment analysis and LSTM." Neural Computing and Applications 32, no. 13 (September 19, 2019): 9713–29. http://dx.doi.org/10.1007/s00521-019-04504-2.

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Dissertations / Theses on the topic "Stock analysis software"

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Žižka, Ladislav. "Aplikace pro analýzu akcií." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-205660.

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This thesis deals with the development of application for fundamental analysis of stocks. Main goal of the thesis is to make application, which will be helpful for individual investors in performing fundamental analysis of stocks. It is desktop application, which performs calculations with high precision and it uses free on-line sources of financial data. The application was developed in Java programming language. It will be available as a~freeware alternative to proprietary fundamental analysis software on the market. The first part describes capital market and stock exchange, makes characteristics of stock and explains principle of fundamental analysis of stocks. In the second part, the market research of fundamental analysis software was realized, design and implementation of the application for fundamental analyiss was described and the application was evaluated.
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Krýcha, Josef. "Fraktální analýza ekonomických časových řad." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-15558.

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This thesis focuses on fractal analysis of economic time series. Chapter One introduces fractal analysis as a method of exploring time series and gathers information about progress and current state of understanding in this field. Chapter Two focuses on design and development of computer software, which will calculate selected fractal indices. Chapter Three is experimental and shows the results and discussion of economic time series (popular stock market indexes and currency exchange rate) analysis that have been obtained from the software developed in Chapter Two.
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Bogren, Patrik, and Isak Kristola. "Exploring the use of call stack depth limits to reduce regression testing costs." Thesis, Mittuniversitetet, Institutionen för data- och systemvetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-43166.

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Regression testing is performed after existing source code has been modified to verify that no new faults have been introduced by the changes. Test case selection can be used to reduce the effort of regression testing by selecting a smaller subset of the test suite for later execution. Several criteria and objectives can be used as constraints that should be satisfied by the selection process. One common criteria is function coverage, which can be represented by a coverage matrix that maps test cases to methods under test. The process of generating and evaluating these matrices can be very time consuming for large matrices since their complexity increases exponentially with the number of tests included. To the best of our knowledge, no techniques for reducing execution matrix size have been proposed. This thesis develops a matrix-reduction technique based on analysis of call stack data. It studies the effects of limiting the call stack depth in terms of coverage accuracy, matrix size, and generation costs. Further, it uses a tool that can instrument Java projects using Java’s instrumentation API to collect coverage information on open-source Java projects for varying depth limits of the call stack. Our results show that the stack depth limit can be significantly reduced while retaining high coverage and that matrix size can be decreased by up to 50%. The metric we used to indicate the difficulty of splitting up the matrix closely resembled the curve for coverage. However, we did not see any significant differences in execution time for lower depth limits.
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Norrby, Elias. "Investigation and Implementation of a Log Management and Analysis Framework for the Treatment Planning System RayStation." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-354921.

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The purpose of this thesis is to investigate and implement a framework for log management and analysis tailored to the treatment planning system (TPS) RayStation. A TPS is a highly advanced software package used in radiation oncology clinics, and the complexity of the software makes writing robust code challenging. Although the product is tested rigorously during development, bugs are present in released software. The purpose of the the framework is to allow the RayStation development team insight into errors encountered in clinics by centralizing log file data recorded at clinics around the world. A framework based on the Elastic stack, a suite of open-source products, is proposed, addressing a set of known issues described as the access problem, the processing problem, and the analysis problem. Firstly, log files are stored locally on each machine running RayStation, some of which may not be connected to the Internet. Gaining access to the data is further complicated by legal frameworks such as HIPAA and GDPR that put constraints on how clinic data can be handled. The framework allows for access to the files while respecting these constraints. Secondly, log files are written in several different formats. The framework is flexible enough to process files of multiple different formats and consistently extracts relevant information. Thirdly, the framework offers comprehensive tools for analyzing the collected data. Deployed in-house on a set of 38 machines used by the RayStation development team, the framework was demonstrated to offer solutions to each of the listed problems.
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Krantz, Karl Johan. "Cross-Platform Post-Mortem Analysis in a Distributed Continuous Integration System." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-122912.

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This thesis aims to improve the cross-platform reliability of software components tested on distributed CI systems. More specifically, it is centered on extracting crash information from cross-platform crashes. Crash information was generated and parsed on Mac, Linux and Windows. The crash information proved to be valuable for developers in their day-to-day job, especially the raw crash information. However, the graph visualizations that were created out of this information proved to be less than satisfactory for developers.
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Chang-Chien, Wan-chun, and 張簡婉君. "A study of consumer behavioral intention for stock investors purchase the stock investment analysis aided software." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/63248431136274998611.

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碩士
國立高雄第一科技大學
金融所
98
Taiwan Stock Market during the past 20 years, there have been three times the quotation passed million points and one time nearly million points. Index peaked at 12,682 in February, 1990, and it reached at 10,256 in August, 1997, at 10,393 in March, 2000, and then ended at the fourth highest 9,859 in October, 2007. The amount of stock trade dropped from 19.03 trillion NT in 1990 to 5.91 trillion NT in 1992. Meanwhile, the peak index 12,682 dropped drastically to 2,485 in September of the same year. The downturn was nearly 10,000 and only took 7 months. The investors suffered great loss because of both global depression and excessive expansion on their credits. Since the drastic fluctuation in the stock market, the investors however have changed the way they invest. For example, they would rather their investments be based on economics information than on tips. This study is to research how and where the investors get their information when they invest, and how accurate the information is. Do the investors agree that they can make their investment easier and more efficient by using an analysis-aided software which is reasonably charged? What are the key factors for the investors consider to buy the software? For the test of a questionnaire for amendments, for the sake of a sample of polyangles, officially released in Kaohsiung County, city Securities Corporation, the stock investors choice of an object includes control is a member of the security companies, banks, by conducting comprehensive security companies, security companies, and local professional dealers. To personally visit the Manager of the security companies, issued a total of 260 applications for anonymous questionnaire, recovery of the iaqmg of 240 copies, not valid answer questionnaire 18 (incomplete), effective questionnaire with a total of 222, effective questionnaire 88.8% recovery rate. In order to recovery the sampled data for statistical analysis, contain sex, marriage, age, education level, occupation, personal recurrent years and investment in the stock market such as singoriensis, used to illustrate the basic characteristics of their. The study design and application of the theory of planned behavior of attitude toward the behavior, subjective norm, perceived behavioral control and consumer behavioral intention in the four variables, to study the schema, for the investor surveys, collect stock market investment “stock market investment analysis aided software” consumer behavioral intention of data, to verify that the theoretical verification of planned behavior of three assumptions. Major findings： (1) In the case of "gender", female investors more acceptable others views and other investors of investment, male investment than objective reason, an analysis of the actual data. (2) In the "invest in singoriensis", ebastine capital allocation, at the same time holding the long and short-term investors than hoped “investmentsin aided software”, to help them achieve greater return, is different from long-term investors hoped “investmentsin aided software” get in and out of field reference information, and short-term investor focus technical and chip surface information, on the fundamentals they don''t care about. (3) To “the average transaction amount to more than 50-100 million ” are concerned, more willing to purchase the “investmentsin aided software”, to enhance the return and reduce risk. (4) "Single-frequency discontinuethese above" investors, a hope that through "investmentsin", assistive software to help them improve profits, and would like to refer to the investment advice and others. (5) The investors of “experience with the use of software” to approve “investmentsin aided software” provide financial news, integrate information read the convenience and savings in information search time and improve the profitability. And never used a software investors selected and guqiang teacher comments, their investment risk a hard to control. According to this major found that, for investors and make recommendations to the software provider for its reference, look forward to investors through appropriate investment analysis software to increase the reward and how to reduce investment risk.
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WATTANASIRITHAM, THAMMANONT, and 許聖明. "Construct a Stock Automatic Analysis Software: The Five Line Stave Decision Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/gdab8w.

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碩士
國立虎尾科技大學
財務金融系碩士班
106
Program trading is a powerful tool to determine financial investment. It is widely used by financial institutions and investors in decision analysis. We use Microsoft Excel Visual Basic program to establish a trading platform for data capture and analysis system. In terms of stock price data retrieval, we capture the stock price data on the Yahoo Finance website and than build the database. In terms of data analysis, we used the five line stave decision model to help us determine the time to buy and sell stocks. Finally, we actually use this platform and get a pretty good reward.
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Books on the topic "Stock analysis software"

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Mesnil, B. Computer programs for fish stock assessment: ANACO : software for the analysis of catch data by age group on IBM PC and compatibles. Rome: Food and Agriculture Organization of the United Nations, 1989.

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Trading index options. New York: McGraw-Hill, 1998.

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Beginners guide to computer assisted trading: How to successfully trade stocks, commodities, and funds with your PC. [Greenville, SC]: Traders Press, 1997.

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Trading Index Options. New York: McGraw-Hill, 2001.

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Book chapters on the topic "Stock analysis software"

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Du, Yuyue, and Changjun Jiang. "Formal Representation and Analysis of Batch Stock Trading Systems by Logical Petri Net Workflows." In Formal Methods and Software Engineering, 221–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36103-0_24.

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Fernandes, Jose A., Zigor Uriondo, Igor Granado, and Iñaki Quincoces. "Tuna Fisheries Fuel Consumption Reduction and Safer Operations." In Big Data in Bioeconomy, 377–88. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_29.

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AbstractThis chapter demonstrates the potential of tuna fishing fleets to reduce their fuel oil consumption. In the “Oceanic tuna fisheries, immediate operational choices” pilot, the data monitoring system on vessels periodically upload data to the server for shore analysis. The data analytics employs fuel oil consumption equations and propulsion engine fault detection models. The fuel consumption equations are being used to develop immediate operational decision models. The fault detection models are used to plan maintenance operations and to prevent unexpected engine malfunctions. The data-driven planning software allows probabilistic forecasting of tuna biomass distribution and analysing changes in fishing strategies leading to fuel consumption reduction. These changes in fishing strategies can be summarized as a transition from hunting to harvesting. Vessels do not search for fish, but instread take less risks and fish, where it is more likely that the fish can be found and is easier to capture. Buoy data are increasingly used to improve stock assessments and have the potential to allow better monitoring and planning of fish quotas fulfilment.
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Mietke, Frank, Robert Rex, Robert Baumgartl, Torsten Mehlan, Torsten Hoefler, and Wolfgang Rehm. "Analysis of the Memory Registration Process in the Mellanox InfiniBand Software Stack." In Euro-Par 2006 Parallel Processing, 124–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11823285_13.

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Petruccioli, Andrea, Francesco Gherardini, Davide Panari, and Francesco Leali. "Computer-Aided Tolerancing Analysis of a High-Performance Car Engine Assembly." In Lecture Notes in Mechanical Engineering, 121–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70566-4_20.

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AbstractThis paper proposes the analysis of the tolerances (values, types, datum) and their effects on a mechanical assembly, as a high-performance car engine, by means of a Computer-Aided Tolerancing software. The 3D tolerance stack-ups are investigated to assess the fulfillment of the functional requirements as well as the performance specifications of the assembly. Moreover, after identifying the tolerances that mainly affect the product variability, we finally propose some corrective actions on the tolerances and assess their functional allocation, tightening or relaxing their values, ensuring assemblability and cost reduction.
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Sisodia, Dilip Singh, and Sagar Jadhav. "Machine Learning Models for Forecasting of Individual Stocks Price Patterns." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 111–29. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3870-7.ch008.

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Stock investors always consider potential future prices before investing in any stock for making a profit. A large number of studies are found on the prediction of stock market indices. However, the focus on individual stock closing price predictions well ahead of time is limited. In this chapter, a comparative study of machine-learning-based models is used for the prediction of the closing price of a particular stock. The proposed models are designed using back propagation neural networks (BPNN), support vector regression (SVR) with SMOReg, and linear regression (LR) for the prediction of the closing price of individual stocks. A total of 37 technical indicators (features) derived from historical closing prices of stocks are considered for predicting the future price of stock in a time window of five days. The experiment is performed on stocks listed on Bombay Stock Exchange (BSS), India. The model is trained and tested using feature values extracted from the past five-year closing price of stocks of different sectors including aviation, pharma, banking, entertainment, and IT.
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Lahmiri, Salim. "On Simulation Performance of Feedforward and NARX Networks Under Different Numerical Training Algorithms." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 171–83. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8823-0.ch005.

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This chapter focuses on comparing the forecasting ability of the backpropagation neural network (BPNN) and the nonlinear autoregressive moving average with exogenous inputs (NARX) network trained with different algorithms; namely the quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno, BFGS), conjugate gradient (Fletcher-Reeves update, Polak-Ribiére update, Powell-Beale restart), and Levenberg-Marquardt algorithm. Three synthetic signals are generated to conduct experiments. The simulation results showed that in general the NARX which is a dynamic system outperforms the popular BPNN. In addition, conjugate gradient algorithms provide better prediction accuracy than the Levenberg-Marquardt algorithm widely used in the literature in modeling exponential signal. However, the LM performed the best when used for forecasting the Moroccan and South African stock price indices under both the BPNN and NARX systems.
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Uğurlu, Erginbay. "Research Data Analysis Using EViews." In Advances in Library and Information Science, 292–324. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8437-7.ch014.

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The aim of this chapter is to provide a detailed empirical example of autoregressive conditional heteroskedasticity (ARCH) model and selected generalized ARCH models. Before the ARCH/GARCH models are estimated, several calculations and tests should be done. The mean model is determined using the autocorrelation function and partial autocorrelation function and also the unit root test. The existence of ARCH effect is tested using ARCH-LM test. After these steps are done, then ARCH/GARCH models can be estimated. All these theoretical aspects are applied to Sofia Stock Indexes (SOFIX) using EViews 9 software package. The windows and output of EViews are presented. To show the output's academic writing format researchers' outputs are presented in a table.
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ElHarakany, Reem A., Alfredo Moscardini, Nermine M. Khalifa, Marwa M. Abd Elghany, and Mona M. Abd Elghany. "The Use of Systems Dynamics in the Analysis of Facilities Management Affecting the Quality of Higher Education in Egypt." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 111–33. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5788-4.ch005.

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In every nation, it is important that the higher education system fulfils the community expectation for perceived quality. One aspect of improving quality is to adopt a better funding strategy for the facilities that are provided by a university. This chapter focuses on the funding strategy for Egyptian universities. The chapter provides the background to the problem distinguishing between private and public universities thenceforward describes the process, employing the causal diagrams, to prioritise the six most important facility services. The budgeting for these services are individually modelled (using both the qualitative and quantitative system dynamics) and then incorporated into a combined stock and flow model. The models allow a facility service administrator to optimise spending in a particular area and also to rank the spending between the six facilities. The models for private and public universities have the same structure but differ with the data. Results of the analysis are discussed in detail.
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Samantaray, Arun K., and Smitirupa Pradhan. "Dynamic Analysis of Steering Bogies." In Advances in Civil and Industrial Engineering, 524–79. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0084-1.ch021.

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Running times of high-speed rolling stock can be reduced by increasing running speed on curved portions of the track. During curving, flange contact causes large lateral force, high frequency noises, flange wears and wheel load fluctuation at transition curves. To avoid derailment and hunting, and to improve ride comfort, i.e., to improve the curving performances at high speed, forced/active steering bogie design is studied in this chapter. The actively steered bogie is able to negotiate cant excess and deficiency. The bogie performance is studied on flexible irregular track with various levels of cant and wheel wear. The bogie and coach assembly models are developed in Adams VI-Rail software. This design can achieve operating speed up to 360 km/h on standard gauge ballasted track with 150mm super-elevation, 4km turning radius and 460m clothoid type entry curve design. The key features of the designed bogie are the graded circular wheel profiles, air-spring secondary suspension, chevron springs in the primary suspension, anti-yaw and lateral dampers, and the steering linkages.
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Pawlowsky-Glahn, Vera, and Richardo A. Olea. "Application to real data." In Geostatistical Analysis of Compositional Data. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195171662.003.0013.

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Methods for spatial correlation analysis and estimation of r-compositions introduced in the foregoing chapters are illustrated here by an example that draws upon real data taken from the Lyons West oil field located in west-central Kansas, USA. Data consist of core analyses of water saturation, saturated thickness and average reservoir porosity over the connate saturated interval at different locations in the Lyons West field. These data are used to compare different possible methods for predicting regionalized compositions. The methods we consider are: 1. a direct approach for estimating compositional variables derived from the original measurements; 2. the basis method, applicable only when there is a random function that can be regarded as the size or accumulation of the regionalized variable under study; 3. the logratio approach, using the additive logratio (air) transformation. Kriging and cokriging estimation methods will be considered for original compositions and for transformed data. Software used for statistical analyses include GSLIB, programs written by Ma and Yao (2001) and ad hoc programs written by the authors. GSLIB is a public-domain library of geostatistical programs written in Fortran (Deutsch and Journel 1998); the other programs are available from their authors. The Lyons West oil field is located at 98° 15' west longitude and 38° 20' north latitude in west-central Kansas, near the center of the United States. The reservoir occurs in Mississippian (Lower Carboniferous) rocks that originated as sediments deposited in the shallow interior sea that covered much of North America in the late Paleozoic. The field was discovered somewhat accidentally in 1963, during the drilling of a deeper Ordovician prospect. Initial oil in place was estimated at 22 million stock-tank barrels of oil. The genesis of the reservoir, composed of carbonate-cemented sands, is interpreted as an offshore bar enclosed in marine shales. Regional uplift tilted the sand body, which was truncated along the western margins by the unconformity marking the base of the Pennsylvanian (Upper Carboniferous). The sandstones interfinger with marine shales to the east, but the eastern margin of the reservoir is defined by the intersection of the oil-water contact with the shale seal at the top of the reservoir interval (Ehm 1965).
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Conference papers on the topic "Stock analysis software"

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Yixin, Zhou, and Jie Zhang. "Stock Data Analysis Based on BP Neural Network." In 2010 Second International Conference on Communication Software and Networks. IEEE, 2010. http://dx.doi.org/10.1109/iccsn.2010.12.

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Meng, Fanxing, Weiyan Song, JiLin Qin, and Chunguang Song. "Analysis of Stock Market Volatility Based on Python." In 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE, 2021. http://dx.doi.org/10.1109/aemcse51986.2021.00212.

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Zhang, Ruixun, Zhaozheng Yuan, and Xiuli Shao. "A New Combined CNN-RNN Model for Sector Stock Price Analysis." In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2018. http://dx.doi.org/10.1109/compsac.2018.10292.

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"Software of Cyclic Analysis and Forecasting in the Example of Stock Prices." In 2015 The 5th International Workshop on Computer Science and Engineering-Information Processing and Control Engineering. WCSE, 2015. http://dx.doi.org/10.18178/wcse.2015.04.040.

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Zhu, Shitao, Ming Zhao, Shengqing Wei, and Simeng An. "Stock Index Prediction Based on Principal Component Analysis and Machine Learning." In 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2020. http://dx.doi.org/10.1109/icbase51474.2020.00059.

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Chen, Yuh-Jen. "Enhancement of stock market forecasting using a technical analysis-based approach." In 2014 5th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2014. http://dx.doi.org/10.1109/icsess.2014.6933664.

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Suksiri, Preuk, Sirapat Chiewchanwattana, and Khamron Sunat. "Application of singular spectrum analysis and kernel-based extreme learning machine for stock price prediction." In 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2016. http://dx.doi.org/10.1109/jcsse.2016.7748873.

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Tingwei Gao, Xiu Li, Yueting Chai, and Youhua Tang. "Deep learning with stock indicators and two-dimensional principal component analysis for closing price prediction system." In 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2016. http://dx.doi.org/10.1109/icsess.2016.7883040.

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Liu, Zhiqiang, and Jie Pu. "Text information management of annual report and stock price crash risk : -- Based on Text Analysis Technology." In 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE, 2021. http://dx.doi.org/10.1109/aemcse51986.2021.00200.

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Campbell, Michael M. "Software Tools to Support Advanced Design Techniques and Processes." In ASME 2008 9th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2008. http://dx.doi.org/10.1115/esda2008-59063.

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Collaboration between engineering and manufacturing can significantly reduce product costs, and increase product quality. The definition, capture and re-use of standard design features, with their associated proven manufacturing processes in the design stage can significantly reduce manufacturing cost and time to market. Today, 3D models are becoming the central repository for more and more of the critical information which is necessary throughout the Product Development Process. Significant process improvements are possible when organizations embrace the idea of a model-centric design approach, where not only geometry and attributes are captured in the 3D CAD model, but also other data relevant downstream data such as GD&T, 3D annotations, and now even manufacturing process information. The strategies for actually machining and producing designs are important assets for companies. Now, existing manufacturing process knowledge can be capture by the manufacturing engineer using XML based template, and through the use of new CAD technology, this knowledge can be attach to design features. The design feature geometry and attributes (along with the embedded process knowledge) can then be made available to the broader organization, through catalogs of company standard design features such as holes, pocket, step, groove, flange, .. etc. During the engineering activities, as the design model evolves, the design engineer is able to re-use these standard features, creating a 3D model that not only includes the geometric definition of the product, but also the validated, proven process by which that geometry can best be produced. Downstream, once the design is handed off to manufacturing, the manufacturing or process engineer has access to tools that will allow him to extract the process information from the 3D model and define rules to automate the creation of the machining process plan for this model. Specific fixtures required for the different steps of the process can be easily developed using the in-process 3D model, which is generated automatically based on stock removal. Multiple scenarios, based on varying machining resources, production quantity and cycle time, can be analyzed, allowing the process engineer to develop and optimized process plan. This model-centric approach, which leverages product and process data re-use, improves product quality and reduces manufacturing process planning and production time. Typical savings are realized in tool design, increased production throughput and savings due to improved process quality from using validated processes prior to production.
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