Academic literature on the topic 'Prices – Data processing'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Prices – Data processing.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Prices – Data processing"
1
A.O., Bello, and Kabari L.G. "Digital Signal Processing for Predicting Stock Prices." British Journal of Computer, Networking and Information Technology 4, no. 2 (September 5, 2021): 12–21. http://dx.doi.org/10.52589/bjcnit-xnp3ubpl.
Full textAbstract:
With the exponential growth of big data and data warehousing, the amount of data collected from various stock markets around the world has increased significantly. It is now impossible to process and analyze data using mathematical techniques and basic statistical calculations to forecast trends such as closing and opening prices, as well as daily stock market lows and highs. The development of smart and automated stock market forecasting systems has made significant progress in recent years. Digital signal processing is required for analysis and preprocessing because of the accuracy and speed with which these large amounts of data must be processed and analyzed. In this paper, we evaluate some of these predictive algorithms based on three parameters such as speed, accuracy and complexity, we analyze the data using the dataset from kaggle.com and we implement these algorithms using pythons. The results of our analysis in this paper shows a significant correlation between the yearly prices until the year 2018 where there is a significant increase in stock price.
2
Jankovics, Peter. "LONG -TERM CHANGES OF MAIN INPUT -OUTPUT PRICES IN THE HUNGARIAN BROILER SECTOR." Annals of the Polish Association of Agricultural and Agribusiness Economists XX, no. 1 (April 4, 2018): 50–57. http://dx.doi.org/10.5604/01.3001.0011.7228.
Full textAbstract:
The article presents changes of the main input-output prices in the Hungarian broiler industry over a period of 30 years, and associated correlations. For the processing of long-term data, a linear regression function, correlation and regression analysis were used. The cereal prices correlate and their changes also correspond with a change in compound feed prices. A close correlation can be found between cereal price and broiler price, whilst the correlation shown between the compound feed price and broiler price is very close. During the examined period, the feed prices increased at a higher rate than the broiler price. It was also established that the current feed and energy price significantly affect day-old chick prices which corresponds with an increase in price of the broiler. Furthermore, a close relation can be found between energy and feed compound prices.
3
chougale, Jeevan, Abhishek Shinde, Ninad Deshmukh, Dhananjay Sawant, and Vaishali Latke. "House Price Prediction using Machine learning and Image Processing." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 18, 2021): 961–65. http://dx.doi.org/10.51201/jusst/21/05280.
Full textAbstract:
We demonstrate that these urban features can be recorded by street views and satellite image data and enhance the estimate of house prices. In order to estimate house prices in London, UK, we recommend a pipeline that uses a deep neural network model to automatically extract visual features from images. In calculating the house price model, we use typical housing characteristics, such as age, size, and accessibility, as well as visual features from Google Street View images and Bing aerial pictures. We see promising outcomes where learning to describe a neighborhood’s urban efficiency facilitates the estimation of house prices, even when generalizing to previously unseen London boroughs. We discuss the use of non-linear vs. linear approaches to combine these signals with traditional house pricing models and explain how the interpretability of linear models helps one to specifically derive the visual desirability of neighborhoods as proxy variables that are both of importance in their own right and can be used as inputs to other econometric methods. This is particularly useful as it can be extended elsewhere after the network has been trained with the training data, enabling us to produce vivid complex maps of the desirability of London streets.
4
Ma, Ping, and Wei Yang Diao. "An Empirical Analysis of Relative Oil Price Shocks and Chinese Net Processing Exports." Advanced Materials Research 347-353 (October 2011): 3098–102. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.3098.
Full textAbstract:
This paper studies the effects of Chinese relative domestic oil prices on net processing exports. Using a set of monthly data ranging from 2002 to 2008, we identify a long-run equilibrium cointegrating relationship between the two inflationary series. The unidirectional short-run Granger causality is running from relative oil prices to net processing exports, while in the long-run, the Granger causality is bidirectional. What is noteworthy is that relative oil price shocks have long-run positive effects on Chinese net processing exports, indicating the existence of an energy cost-driven mechanism of endogenous technological change.
5
Ekwunife, Ifunanya C. "Technology Focus: Natural Gas Processing and Handling (April 2021)." Journal of Petroleum Technology 73, no. 04 (April 1, 2021): 34. http://dx.doi.org/10.2118/0421-0034-jpt.
Full textAbstract:
In 2020, the spot prices of natural gas hit a record low in the US, reaching the lowest annual average price in more than a decade. Based on US Energy Information Administration (EIA) data, the average annual spot price reported in 2020 was $2.05 per million British thermal units (MMBtu). In the first few months of the year, reports from the EIA showed that natural gas prices started declining amid mild winter temperatures that resulted in a decline in the demand for natural gas for space heating. In March 2020, following the onset of the COVID-19 pandemic, the already declining natural gas prices plummeted further. This decline continued through the first half of the year. The EIA reported the average monthly Henry Hub spot price in the first 6 months at $1.81/MMBtu. June saw the lowest monthly natural gas price in decades (Henry Hub price aver-aged $1.66/MMBtu). Natural gas prices recovered in the second half of the year as natural gas production decreased and global exports of liquefied natural gas increased. Natural gas consumption in the residential, commercial, and industrial sectors declined in 2020, according to the EIA. Milder winter temperatures were a major contributor in the first quarter of the year, but overall declining consumption was attributed to reduced economic activities as a result of the COVID-19 pandemic. On the other hand, the consumption of natural gas for electric power generation registered an overall increase of 2% more than the 2019 average. According to the EIA, citing S&P Global Platts, this increase was attributed to power producers switching to cheaper natural gas from coal to meet the increased demand for electric power for cooling as summer temperatures increased. The EIA in its Annual Energy Outlook 2021 projects that the industrial and electric power sectors and net exports will drive the growth in US energy consumption between 2020 and 2050. Natural gas consumption in other sectors is expected to increase steadily or remain flat. The EIA forecasts that natural gas production will increase as consumption increases and prices will stay low relative to past prices. The EIA expects continued growth in natural gas exports as natural gas production surpasses natural gas consumption. Globally, the International Energy Agency forecasts a recovery in global demand for natural gas in 2021 led by growth in the Asia Pacific region as emerging markets recover. The US will continue to play a significant role as one of the largest producers and contributors to natural gas supply growth. Recommended additional reading at OnePetro: www.onepetro.org. SPE 200300 - Overcoming Challenges in the Development of Underground Gas Storage by Ammar Alali, Saudi Aramco, et al. OTC 30602 - Offshore LNG and Gas Monetization by Femi Adeoye Alabi, Total SPE 200147 - Development of the Underground Gas Storage and Construction of the Salt Cavern Storage in China by Peng Chen, CNPC, et al.
6
Spoden, Amanda L., James H. Buszkiewicz, Adam Drewnowski, Mark C. Long, and Jennifer J. Otten. "Seattle’s minimum wage ordinance did not affect supermarket food prices by food processing category." Public Health Nutrition 21, no. 9 (February 7, 2018): 1762–70. http://dx.doi.org/10.1017/s1368980017004037.
Full textAbstract:
AbstractObjectiveTo examine the impacts of Seattle’s minimum wage ordinance on food prices by food processing category.DesignSupermarket food prices were collected for 106 items using a University of Washington Center for Public Health Nutrition market basket at affected and unaffected supermarket chain stores at three times: March 2015 (1-month pre-policy enactment), May 2015 (1-month post-policy enactment) and May 2016 (1-year post-policy enactment). Food items were categorized into four food processing groups, from minimally to ultra-processed. Data were analysed across time using a multilevel, linear difference-in-differences model at the store and price level stratified by level of food processing.SettingSix large supermarket chain stores located in Seattle (‘intervention’) affected by the policy and six same-chain but unaffected stores in King County (‘control’), Washington, USA.SubjectsOne hundred and six food and beverage items.ResultsThe largest change in average price by food item was +$US 0·53 for ‘processed foods’ in King County between 1-month post-policy and 1-year post-policy enactment (P < 0·01). The smallest change was $US 0·00 for ‘unprocessed or minimally processed foods’ in Seattle between 1-month post-policy and 1-year post-policy enactment (P = 0·94). No significant changes in averaged chain prices were observed across food processing level strata in Seattle v. King County stores at 1-month or 1-year post-policy enactment.ConclusionsSupermarket food prices do not appear to be differentially impacted by Seattle’s minimum wage ordinance by level of the food’s processing. These results suggest that the early implementation of a city-level minimum wage policy does not alter supermarket food prices by level of food processing.
7
Li, Jung Bin, and Chien Ho Wu. "An Efficient Neural Network Model with Taylor Series-Based Data Pre-Processing for Stock Price Forecast." Applied Mechanics and Materials 284-287 (January 2013): 3020–24. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3020.
Full textAbstract:
This study adopts popular back-propagation neural network to make one-period-ahead prediction of the stock price. A model based on Taylor series by using both fundamental and technical indicators EPS and MACD as input data is built for an empirical study. Leading Taiwanese companies in non-hi-tech industry such as Formosa Plastics, Yieh Phui Steel, Evergreen Marine, and Chang Hwa Bank are picked as targets to analyze their reasonable prices and moving trends. The performance of this model shows remarkable return and high accuracy in making long/short strategies.
8
Zakiah, Zakiah. "Preferensi dan Permintaan Kedelai pada Industri dan Implikasinya terhadap Manajemen Usaha Tani." MIMBAR, Jurnal Sosial dan Pembangunan 28, no. 1 (June 19, 2012): 77. http://dx.doi.org/10.29313/mimbar.v28i1.341.
Full textAbstract:
This paper studies demand and preference of soybean processing industry. We used two types of data: time series and primary data that obtained from soybean processing industry. The result shows that increasing of local soybean price will reduce demand for soybeans. Increasing of tempe price and imported soybean price will increase soybean demand, and statistically, the effect is significant. Increasing imported soybean prices should be decrese demand for soybeans at industry, but in this study does not decrease demand for soybeans. This is shows dependence of soybean processing industry in Banda Aceh on imported soybean. To increase local soybean production both in quality and quantity require better farming management, through technological improvements form production stage to harvest, marketing channel, institutional, and decent price for farmers.
9
Razzakova, Ch M., and L. E. Ziganshina. "Change in affordability of medications in Kazan in 2011 and 2015 as a reflection of state initiatives to regulate drug prices." Kazan medical journal 98, no. 5 (October 15, 2017): 822–26. http://dx.doi.org/10.17750/kmj2017-822.
Full textAbstract:
Aim. Performing comparative analysis of drug prices in 2011 and 2015 in Kazan to assess the effectiveness of state initiatives to ensure the affordability of medicines.
Methods. The collection and processing of data was performed according to methodology developed by Health Action International and World Health Organization (WHO/HAI). We studied the availability and prices of 30 medicines in public and private pharmacies in Kazan in 2011 and 2015 and analyzed the procurement prices of the same medicines in inpatient hospitals. Recording and analysis were performed using standardized MS Excel WHO/HAI Workbook. Medicine prices were compared with international reference prices and were expressed as median price ratio.
Results. The analysis showed a decrease in medicine prices in 2015 compared to 2011. In public and private sectors median price ratio of the originator brands reduced by about 3 times, and of the lowest price generics reduced by 1.5 times. A decrease in procurement prices by more than 2 times for generics and more than 6 times for the original brands was also revealed in 2015 in comparison with 2011.
Conclusion. State initiatives to regulate drug prices contributed to the price reduction by 1.5-3 times in 2015 compared to 2011; changes in the procedures for the medicines procurement at the legislative level resulted in reduction of procurement prices by more than 2 times for generic drugs in 2015 compared to 2011.
10
Eni, Yuli, and Rudy Aryanto. "Analysis of Factors that Affect the Movement of Gold’s Price as Investment Alternatives in Indonesia." Advanced Science Letters 21, no. 4 (April 1, 2015): 878–81. http://dx.doi.org/10.1166/asl.2015.5912.
Full textAbstract:
This study examined the dominant factors that affecting the price of gold. The factors examined are London Gold price returns, the return rate of USD—INR, JCI return, inflation rate, and the return of the EURO—USD currency, which individually or simultaneously can affect the price of gold. The purpose of this study was to investigate how influence the factors that are considered to affect the fluctuation of gold prices and gold prices predicted for the next period which can be used by investors to seek alternative investment to be made. The results will provide information to investors about gold price forecast both long-term and short-term. This study uses secondary data taken from several websites. Further data have been obtained, processed using the method of Multiple Linear Regression Model and the ECM with GARCH models, using e-views 8 and SPSS 22. As for the results obtained from the processing of the data is simultaneously the influence of variable returns no London Gold price, return rate USD—CAD, JCI return, inflation rate, and the return of the EURO currency—USD, with the return of gold in Indonesia. Individually, the variable returns the London Gold price and exchange rate USD—CAD who have an influence on the return of gold prices in Indonesia.
Dissertations / Theses on the topic "Prices – Data processing"
1
Labuschagne, Jan Phillipus Lourens. "Development of a data processing toolkit for the analysis of next-generation sequencing data generated using the primer ID approach." University of the Western Cape, 2018. http://hdl.handle.net/11394/6736.
Full textAbstract:
Philosophiae Doctor - PhDSequencing an HIV quasispecies with next generation sequencing technologies yields a dataset with significant amplification bias and errors resulting from both the PCR and sequencing steps. Both the amplification bias and sequencing error can be reduced by labelling each cDNA (generated during the reverse transcription of the viral RNA to DNA prior to PCR) with a random sequence tag called a Primer ID (PID). Processing PID data requires additional computational steps, presenting a barrier to the uptake of this method. MotifBinner is an R package designed to handle PID data with a focus on resolving potential problems in the dataset. MotifBinner groups sequences into bins by their PID tags, identifies and removes false unique bins, produced from sequencing errors in the PID tags, as well as removing outlier sequences from within a bin. MotifBinner produces a consensus sequence for each bin, as well as a detailed report for the dataset, detailing the number of sequences per bin, the number of outlying sequences per bin, rates of chimerism, the number of degenerate letters in the final consensus sequences and the most divergent consensus sequences (potential contaminants). We characterized the ability of the PID approach to reduce the effect of sequencing error, to detect minority variants in viral quasispecies and to reduce the rates of PCR induced recombination. We produced reference samples with known variants at known frequencies to study the effectiveness of increasing PCR elongation time, decreasing the number of PCR cycles, and sample partitioning, by means of dPCR (droplet PCR), on PCR induced recombination. After sequencing these artificial samples with the PID approach, each consensus sequence was compared to the known variants. There are complex relationships between the sample preparation protocol and the characteristics of the resulting dataset. We produce a set of recommendations that can be used to inform sample preparation that is the most useful the particular study. The AMP trial infuses HIV-negative patients with the VRC01 antibody and monitors for HIV infections. Accurately timing the infection event and reconstructing the founder viruses of these infections are critical for relating infection risk to antibody titer and homology between the founder virus and antibody binding sites. Dr. Paul Edlefsen at the Fred Hutch Cancer Research Institute developed a pipeline that performs infection timing and founder reconstruction. Here, we document a portion of the pipeline, produce detailed tests for that portion of the pipeline and investigate the robustness of some of the tools used in the pipeline to violations of their assumptions.
2
Seshadri, Mukund. "Comprehensibility, overfitting and co-evolution in genetic programming for technical trading rules." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0430103-121518.
Full textAbstract:
Thesis (M.S.)--Worcester Polytechnic Institute.Keywords: comprehensiblity; technical analysis; genetic programming; overfitting; cooperative coevolution. Includes bibliographical references (p. 82-87).
3
Booyens, Johann Grebe. "The software ideated plate : towards designing a new relationship of integration between digital technology and the intaglio process." Thesis, Cape Peninsula University of Technology, 2014. http://hdl.handle.net/20.500.11838/1329.
Full textAbstract:
Dissertation submitted in partial fulfilment of the requirements for the degree
Master of Technology: Graphic Design in the Faculty of Informatics and Design at
the Cape Peninsula University of TechnologyThis study investigates the application and use of the latest graphic design software technologies to help plan and ideate the intaglio printmaking process. This is significant as intaglio is a 600 year old process which has evolved little, if any, in the last few hundred years although it was born from technology. Furthermore, the intaglio process relies on mental visualisation of the final artwork, making the real outcome and the planned outcome dissimilar. Students of intaglio printmaking are often surprised or disappointed by the printed result due to the lack of efficient planning. There are several ways in which software influences the creative process, including enhancing visualisation and communication, premature fixation, circumscribed thinking and bounded ideation. In this research, computer software is used as a simulator to facilitate the planning process in order to minimise the disconnect between visualisation and outcome, and serve as learning instrument. The use of digital computer technologies has been a highly debated issue in printmaking as there exists a rift between printmakers; those who embrace and explore new technologies and those who reject new methods in favour of traditional means. New technologies in printmaking offer exciting opportunities, both innovative and creative, but these new technologies are often seen as alternative or auxiliary methods of printmaking compared to traditional ways. Since these debates have been buried but not necessarily resolved, this study reinvigorates some of these perspectives and seeks a common middle ground. This study does not argue for, or against computer technology, but rather for a third paradigm: technology can coexist with intaglio without compromising the beauty and authenticity of hand processes. Computer technologies, therefore, serve as a facilitator to amplify the traditional intaglio hand process. However, the issue of discussion in this thesis is not hybrid printmaking but rather a hybrid mode of thinking in the printmaking discipline. This iterative design experiment consists of a written dissertation and intaglio printed artworks which inform and complement each other. The theoretical foundation of the art practice is found in the Bauhaus slogan: “Art and technology: a new unity”. Art and technology form the basis of the theory and the theme of entropy – the process of degeneration – is illustrated in the design artefacts. This theme shows process and illustrates the idea of a positive agent: the interference of computer in intaglio to instil new energy and value not only to keep it alive, but position it as an important skill necessary for growth in the knowledge-based economy. Furthermore, this study contributes to the scholarly discussion of design’s conceptual skills (ways of thinking) in order to enhance production capabilities (ways of making).
4
Abufadel, Amer Y. "4D Segmentation of Cardiac MRI Data Using Active Surfaces with Spatiotemporal Shape Priors." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14005.
Full textAbstract:
This dissertation presents a fully automatic segmentation algorithm for cardiac MR data. Some of the currently published methods are
automatic, but they only work well in 2D and sometimes in 3D and do not perform well near the extremities (apex and base) of the heart.
Additionally, they require substantial user input to make them feasible for use in a clinical environment. This dissertation introduces
novel approaches to improve the accuracy, robustness, and consistency of existing methods.
Segmentation accuracy can be improved by knowing as much about the data as possible. Accordingly, we compute a single 4D active surface
that performs segmentation in space and time simultaneously. The segmentation routine can now take advantage of information from
neighboring pixels that can be adjacent either spatially or temporally.
Robustness is improved further by using confidence labels on shape priors. Shape priors are deduced from manual
segmentation of training data. This data may contain imperfections that may impede proper manual segmentation. Confidence
labels indicate the level of fidelity of the manual segmentation to the actual data. The contribution of regions with low
confidence levels can be attenuated or excluded from the final result.
The specific advantages of using the 4D segmentation along with shape priors and regions of confidence are highlighted throughout the
thesis dissertation. Performance of the new method is measured by comparing the results to traditional 3D segmentation and to manual
segmentation performed by a trained clinician.
5
Ivancic, Lorraine Economics Australian School of Business UNSW. "Scanner data and the construction of price indices." 2007. http://handle.unsw.edu.au/1959.4/40782.
Full textAbstract:
This thesis explores whether scanner data can be used to inform Consumer Price Index (CPI) construction, with particular reference to the issues of substitution bias and choice of aggregation dimensions. The potential costs and benefits of using scanner data are reviewed. Existing estimates of substitution bias are found to show considerable variation. An Australian scanner data set is used to estimate substitution bias for six different aggregation methods and for fixed base and superlative indexes. Direct and chained indexes are also calculated. Estimates of substitution bias are found to be highly sensitive to both the method of aggregation used and whether direct or chained indexes were used. The ILO (2004) recommends the use of dissimilarity indexes to determine the issue of when to chain. This thesis provides the first empirical study of dissimilarity indexes in this context. The results indicate that dissimilarity indexes may not be sufficient to resolve the issue. A Constant Elasticity of Substitution (CES) index provides an approximate estimate of substitution-bias-free price change, without the need for current period expenditure weights. However, an elasticity parameter is needed. Two methods, referred to as the algebraic and econometric methods, were used to estimate the elasticity parameter. The econometric approach involved the estimation of a system of equations proposed by Diewert (2002a). This system has not been estimated previously. The results show a relatively high level of substitution at the elementary aggregate level, which supports the use a Jevons index, rather than Carli or Dutot indexes, at this level. Elasticity parameter estimates were found to vary considerably across time, and statistical testing showed that elasticity parameter estimates were significantly different across estimation methods. Aggregation is an extremely important issue in the compilation of the CPI. However, little information exists about 'appropriate' aggregation methods. Aggregation is typically recommended over 'homogenous' units. An hedonic framework is used to test for item homogeneity across four supermarket chains and across all stores within each chain. This is a novel approach. The results show that treating the same good as homogenous across stores which belong to the same chain may be recommended.
6
"Application of neural network to study share price volatility." 1999. http://library.cuhk.edu.hk/record=b5896263.
Full textAbstract:
by Lam King Wan.Thesis (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
7
De, Villiers J. "The use of neural networks to predict share prices." Thesis, 2012. http://hdl.handle.net/10210/6001.
Full textAbstract:
M.Comm.The availability of large amounts of information and increases in computing power have facilitated the use of more sophisticated and effective technologies to analyse financial markets. The use of neural networks for financial time series forecasting has recently received increased attention. Neural networks are good at pattern recognition, generalisation and trend prediction. They can learn to predict next week's Dow Jones or flaws in concrete. Traditional methods used to analyse financial markets include technical and fundamental analysis. These methods have inherent shortcomings, which include bad timing of trading signals generated, and non-continuous data on which analysis is based. The purpose of the study was to create a tool with which to forecast financial time series on the Johannesburg Stock Exchange (JSE). The forecasted time series information was used to generate trading signals. A study of the building blocks of neural networks was done before the neural network was designed. The design of the neural network included data choice, data collection, calculations, data pre-processing and the determination of neural network parameters. The neural network was trained and tested with information from the financial sector of the JSE. The neural network was trained to predict share prices 4 days in advance with a Multiple Layer Feedforward Network (MLFN). The mean square error on the test set was 0.000930, with all test data values scaled between 0.1 - 0.9 and a sample size of 160. The prediction results were tested with a trading system, which generated a trade yielding 20 % return in 22 days. The neural network generated excellent results by predicting prices in advance. This enables better timing of trades and efficient use of capital. However, it was found that the price movement on the test set within the 4-day prediction period seldom exceeded the cost of trades, resulting in only one trade over a 5-month period for one security. This should not be a problem if all securities on the JSE are analysed for profitable trades. An additional neural network could also be designed to predict price movements further ahead, say 8 days, to assist the 4-day prediction
8
"Hedonic property valuation using geographic information system in Hong Kong." Chinese University of Hong Kong, 1996. http://library.cuhk.edu.hk/record=b5888889.
Full textAbstract:
by Vera Hau Tsz Lai.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.
Includes bibliographical references (leaves 227-236).
ABSTRACT --- p.i-ii
ACKNOWLEDGEMENTS --- p.iii-iv
TABLE OF CONTENTS --- p.v-ix
LIST OF FIGURES --- p.x
LIST OF PLATES --- p.xi-xiii
LIST OF TABLES --- p.xiv-xvi
Chapter CHAPTER I --- INTRODUCTION --- p.1
Chapter 1.1 --- Problem Statement --- p.1
Chapter 1.2 --- Role of GIS in Housing Price Study --- p.3
Chapter 1.3 --- Research Objectives --- p.4
Chapter 1.4 --- Significance --- p.5
Chapter 1.5 --- Methodologies --- p.6
Chapter 1.6 --- Organization of Thesis --- p.7
Chapter CHAPTER II --- LITERATURE REVIEW --- p.9
Chapter 2.1 --- Introduction --- p.9
Chapter 2.2 --- Geography of Housing --- p.10
Chapter 2.3 --- Housing as a Research Question --- p.11
Chapter 2.4 --- Housing Services and Housing Price --- p.12
Chapter 2.5 --- Property Price Valuation --- p.14
Chapter 2.6 --- Hedonic Price Function --- p.15
Chapter 2.6.1 --- Dependent Variable - Property Price --- p.16
Chapter 2.6.2 --- Independent Variables Affecting Housing Price --- p.17
Chapter 2.6.2.1 --- Aspatial Factors --- p.17
Chapter 2.6.2.2 --- Spatial Factors --- p.18
Chapter 2.6.2.3 --- Evaluation on Importance of Parameters --- p.26
Chapter 2.7 --- Functional Form of Hedonic Price Models --- p.33
Chapter 2.7.1 --- Conventional Specifications --- p.34
Chapter 2.7.2 --- Box-Cox Transformation --- p.34
Chapter 2.7.3 --- Conventional Specifications versus Box-Cox Transformation --- p.35
Chapter 2.8 --- Submarket Analysis and its Delineation --- p.36
Chapter 2.9 --- Geographic Information Systems --- p.39
Chapter 2.10 --- GIS in Real Estate --- p.39
Chapter 2.11 --- Present Adoption of GIS in Real Estate --- p.42
Chapter 2.11.1 --- Commercial Applications --- p.42
Chapter 2.11.2 --- Research-wise Applications --- p.43
Chapter 2.12 --- Hedonic Price Study with GIS --- p.43
Chapter 2.13 --- Conclusion --- p.45
Chapter CHAPTER III --- THE STUDY AREA AND RESEARCH METHODOLOGY --- p.47
Chapter 3.1 --- Introduction --- p.47
Chapter 3.2 --- Real Estate Sector in Hong Kong --- p.47
Chapter 3.2.1 --- Importance to Local Economy --- p.48
Chapter 3.2.2 --- Importance to Housing Production --- p.48
Chapter 3.3 --- Urban Development and Housing in Hong Kong --- p.51
Chapter 3.3.1 --- Land Availability and Landuses --- p.51
Chapter 3.3.2 --- Housing and Urban Development --- p.54
Chapter 3.3.2.1 --- Early Period of Industrialization --- p.54
Chapter 3.3.2.2 --- Phase of Economic Restructuring --- p.55
Chapter 3.3.3 --- Urban Renewal --- p.55
Chapter 3.3.4 --- Comprehensive Housing Projects --- p.56
Chapter 3.4 --- New Town Housing - Public or Private-Led --- p.57
Chapter 3.5 --- Hedonic Price of Private Dormitory in Hong Kong --- p.61
Chapter 3.5.1 --- Temporal Change in Property Price --- p.62
Chapter 3.5.2 --- Spatial Variation of Property Price --- p.66
Chapter 3.6 --- The Research --- p.68
Chapter 3.6.1 --- Cartographic Analysis --- p.68
Chapter 3.6.2 --- Hedonic Price Model --- p.69
Chapter 3.6.3 --- Dependent Variable --- p.69
Chapter 3.6.4 --- Independent Variables --- p.70
Chapter 3.6.5 --- Chosen Functional Form in this Research --- p.72
Chapter 3.6.6 --- Submarket Analysis in Hong Kong --- p.72
Chapter 3.7 --- Conclusion --- p.72
Chapter CHAPTER IV --- DATABASE CONSTRUCTIONS --- p.74
Chapter 4.1 --- Introduction --- p.74
Chapter 4.2 --- Data Collection --- p.74
Chapter 4.2.1 --- Base Maps --- p.75
Chapter 4.2.2 --- Housing Stock and its Attributes --- p.76
Chapter 4.2.3 --- Official Statistics --- p.76
Chapter 4.2.4 --- School Quality --- p.77
Chapter 4.3 --- Data Input --- p.78
Chapter 4.3.1 --- Graphical Input --- p.78
Chapter 4.3.1.1 --- Base Maps --- p.78
Chapter 4.3.1.2 --- Line Data --- p.78
Chapter 4.3.1.3 --- Point/Polygon Data --- p.79
Chapter 4.3.2 --- Attribute Data Input --- p.82
Chapter 4.4 --- Data Editing and Conversions --- p.82
Chapter 4.4.1 --- Graphical Input --- p.82
Chapter 4.4.1.1 --- Standard Coverage Editing Procedures --- p.82
Chapter 4.4.1.2 --- Specific Coverage Editing Procedures --- p.83
Chapter 4.4.2 --- Attribute Data --- p.84
Chapter 4.4.2.1 --- Housing Attributes --- p.84
Chapter 4.4.2.2 --- Landuse Mix --- p.88
Chapter 4.4.2.3 --- Socioeconomic Status --- p.91
Chapter 4.4.2.4 --- Employment Figures --- p.91
Chapter 4.5 --- Data Pre-processing and Manipulation --- p.93
Chapter 4.5.1 --- Employment Potentials --- p.93
Chapter 4.5.2 --- Socioeconomic Variables --- p.96
Chapter 4.5.2.1 --- Interpretation --- p.97
Chapter 4.5.3 --- School Quality --- p.107
Chapter 4.5.4 --- Proximity Measurements --- p.110
Chapter 4.5.5 --- Final Step of Association : Overlay Operations --- p.110
Chapter 4.6 --- Conclusion --- p.112
Chapter CHAPTER V --- CARTOGRAPHIC ANALYSIS --- p.114
Chapter 5.1 --- Introduction --- p.114
Chapter 5.2 --- Representation of Data --- p.114
Chapter 5.2.1 --- Location of Premises --- p.114
Chapter 5.2.2 --- Proximity --- p.118
Chapter 5.2.3 --- School Quality --- p.118
Chapter 5.2.4 --- Landuse Mix --- p.129
Chapter 5.2.5 --- Employment --- p.132
Chapter 5.2.6 --- Property Price --- p.137
Chapter 5.3 --- Results and Discussions --- p.137
Chapter 5.3.1 --- Temporal Variation on Housing Supply --- p.143
Chapter 5.3.2 --- Temporal Variation on Floor Size --- p.145
Chapter 5.3.3 --- Temporal Variation on Property Price --- p.148
Chapter 5.4 --- Locational Variations --- p.150
Chapter 5.4.1 --- Shift towards the New Towns --- p.150
Chapter 5.4.2 --- Relative Importance among Districts in New Towns --- p.154
Chapter 5.4.3 --- Pattern of Development --- p.158
Chapter 5.4.3.1 --- Urban Core --- p.158
Chapter 5.4.3.2 --- New Towns --- p.161
Chapter 5.5 --- Spatial Variations on Floor Size --- p.171
Chapter 5.6 --- Spatial Variations on Property Price --- p.176
Chapter 5.7 --- Conclusion --- p.181
Chapter CHAPTER VI --- STATISTICAL ANALYSIS --- p.183
Chapter 6.1 --- Introduction --- p.183
Chapter 6.2 --- The Data Set --- p.183
Chapter 6.3 --- Stepwise Regression Modeling --- p.184
Chapter 6.4 --- Correlation among Variables --- p.184
Chapter 6.5 --- Validation of the Models --- p.186
Chapter 6.6 --- Findings --- p.193
Chapter 6.6.1 --- Pooled Market Results --- p.193
Chapter 6.6.2 --- Submarket Level Analyses --- p.198
Chapter 6.6.2.1 --- "Small-Sized, Low-Priced Flats " --- p.200
Chapter 6.6.2.2 --- "Small-Sized, High-Priced Flats " --- p.203
Chapter 6.6.2.3 --- "Medium-Sized, Low-Priced Flats " --- p.206
Chapter 6.6.2.4 --- "Medium-Sized, High-Priced Flats " --- p.210
Chapter 6.6.2.5 --- "Large-Sized, High-Priced Flats " --- p.213
Chapter 6.7 --- Conclusion --- p.213
Chapter CHAPTER VII --- CONCLUSION --- p.217
Chapter 7.1 --- Summary of Findings --- p.217
Chapter 7.1.1 --- Summary on Housing Development in Hong Kong…… --- p.217
Chapter 7.1.2 --- Summary from Hedonic Price Models --- p.220
Chapter 7.1.3 --- Significance of GIS --- p.222
Chapter 7.2 --- Limitations and Recommendations --- p.222
Chapter 7.3 --- Direction of Future Research --- p.226
BIBLIOGRAPHY --- p.227
APPENDICES --- p.237
APPENDIX 1 --- p.238
District Map of Hong Kong --- p.239
APPENDIX II --- p.240
List of Districts and its Components --- p.241
APPENDIX III --- p.243
Tertiary Planning Units (TPUs) - District Conversion List --- p.244
9
"Price discovery of stock index with informationally-linked markets using artificial neural network." 1999. http://library.cuhk.edu.hk/record=b5889930.
Full textAbstract:
by Ng Wai-Leung Anthony.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.
Includes bibliographical references (leaves 83-87).
Abstracts in English and Chinese.
Chapter I. --- INTRODUCTION --- p.1
Chapter II. --- LITERATURE REVIEW --- p.5
Chapter 2.1 --- The Importance of Stock Index and Index Futures --- p.6
Chapter 2.2 --- Importance of Index Forecasting --- p.6
Chapter 2.3 --- Reasons for the Lead-Lag Relationship between Stock and Futures Markets --- p.9
Chapter 2.4 --- Importance of the lead-lag relationship --- p.10
Chapter 2.5 --- Some Empirical Findings of the Lead-Lag Relationship --- p.10
Chapter 2.6 --- New Approach to Financial Forecasting - Artificial Neural Network --- p.12
Chapter 2.7 --- Artificial Neural Network Architecture --- p.14
Chapter 2.8 --- Evidence on the Employment of ANN in Financial Analysis --- p.20
Chapter 2.9 --- Hong Kong Securities and Futures Markets --- p.25
Chapter III. --- GENERAL GUIDELINE IN DESIGNING AN ARTIFICIAL NEURAL NETWORK FORECASTING MODEL --- p.28
Chapter 3.1 --- Procedure for using Artificial Neural Network --- p.29
Chapter IV. --- METHODOLOGY --- p.37
Chapter 4.1 --- ADF Test for Unit Root --- p.38
Chapter 4.2 --- "Error Correction Model, Error Correction Model with Short- term Dynamics, and ANN Models for Comparisons" --- p.38
Chapter 4.3 --- Comparison Criteria of Different Models --- p.39
Chapter 4.4 --- Data Analysis --- p.39
Chapter 4.5 --- Data Manipulations --- p.41
Chapter V. --- RESULTS --- p.42
Chapter 5.1 --- The Resulting Models --- p.42
Chapter 5.2 --- The Prediction Power among the Models --- p.45
Chapter 5.3 --- ANN Model of Input Variable Selection Using Contribution Factor --- p.46
Chapter VI. --- CAUSALITY ANALYSIS --- p.54
Chapter 6.1 --- Granger Casuality Analysis --- p.55
Chapter 6.2 --- Results Interpretation --- p.56
Chapter VII --- CONSISTENCE VALIDATION --- p.61
Chapter VIII --- ARTIFICIAL NEURAL NETWORK TRADING SYSTEM --- p.67
Chapter 7.1 --- Trading System Architecture --- p.68
Chapter 7.2 --- Simulation Runs using the Trading System --- p.77
Chapter XI. --- CONCLUSIONS AND FUTURE WORKS --- p.79
10
"A study of genetic fuzzy trading modeling, intraday prediction and modeling." Thesis, 2010. http://library.cuhk.edu.hk/record=b6074843.
Full textAbstract:
This thesis consists of three parts: a genetic fuzzy trading model for stock trading, incremental intraday information for financial time series forecasting, and intraday effects in conditional variance estimation. Part A investigates a genetic fuzzy trading model for stock trading. This part contributes to use a fuzzy trading model to eliminate undesirable discontinuities, incorporate vague trading rules into the trading model and use genetic algorithm to select an optimal trading ruleset. Technical indicators are used to monitor the stock price movement and assist practitioners to set up trading rules to make buy-sell decision. Although some trading rules have a clear buy-sell signal, the signals are always detected with 'hard' logical. These trigger the undesirable discontinuities due to the jumps of the Boolean variables that may occur for small changes of the technical indicator. Some trading rules are vague and conflicting. They are difficult to incorporate into the trading system while they possess significant market information. Various performance comparisons such as total return, maximum drawdown and profit-loss ratios among different trading strategies were examined. Genetic fuzzy trading model always gave moderate performance. Part B studies and contributes to the literature that focuses on the forecasting of daily financial time series using intraday information. Conventional daily forecast always focuses on the use of lagged daily information up to the last market close while neglecting intraday information from the last market close to current time. Such intraday information are referred to incremental intraday information. They can improve prediction accuracy not only at a particular instant but also with the intraday time when an appropriate predictor is derived from such information. These are demonstrated in two forecasting examples, predictions of daily high and range-based volatility, using linear regression and Neural Network forecasters. Neural Network forecaster possesses a stronger causal effect of incremental intraday information on the predictand. Predictability can be estimated by a correlation without conducting any forecast. Part C explores intraday effects in conditional variance estimation. This contributes to the literature that focuses on conditional variance estimation with the intraday effects. Conventional GARCH volatility is formulated with an additive-error mean equation for daily return and an autoregressive moving-average specification for its conditional variance. However, the intra-daily information doesn't include in the conditional variance while it should has implication on the daily variance. Using Engle's multiplicative-error model formulation, range-based volatility is proposed as an intraday proxy for several GARCH frameworks. The impact of significant changes in intraday data is reflected in the MEM-GARCH variance. For some frameworks, it is possible to use lagged values of range-based volatility to delay the intraday effects in the conditional variance equation.Ng, Hoi Shing Raymond.
Adviser: Kai-Pui Lam.
Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2010.
Includes bibliographical references (leaves 107-114).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
Books on the topic "Prices – Data processing"
1
Chekhlov, N. I. T͡S︡ena na ėkrane kompʹi͡u︡tera. Moskva: "Ėkonomika", 1988.
Find full text
2
missing], [name. Scanner data and price indexes. Chicago, IL: University of Chicago Press, 2002.
Find full text
3
Holland, F. D. GTP: A microcomputer program for the spatial equilibrium problem. [Washington, D.C.]: U.S. Dept. of Agriculture, Economic Research Service, International Economics Division, 1985.
Find full text
4
Chmelik, John T. Softwood lumber prices for evaluation of small-diameter timber stands in the Intermountain West. Madison, WI: U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, 1999.
Find full text
5
A, Pape Larry, ed. Demassification: A cost comparison of micro vs. mini : why conventional computer wisdom may not be wisdom at all. Minneapolis, Minn: Fourth Shift Corp., 1989.
Find full text
6
Khubaev, G. N. Modeli, metody i programmnyĭ instrumentariĭ ot︠s︡enki sovokupnoĭ stoimosti vladenii︠a︡ obʺektami dlitelʹnogo polʹzovanii︠a︡ (na primere programmnykh sistem): Monografii︠a︡. Rostov-na-Donu: Rostovskiĭ gosudarstvennyĭ ėkonomicheskiĭ universitet, 2011.
Find full text
7
Preisbildung und Informationsverarbeitung im Optionsmarkt: Untersuchungen zur Schweizerischen Options- und Futuresbörse (SOFFEX). Bern: Verlag Paul Haupt, 1998.
Find full text
8
Hansen, Bruce G. System 6: A pricing strategy for long blanks. [Broomall, PA]: U.S. Dept. of Agriculture, Forest Service, Northeastern Forest Experiment Station, 1986.
Find full text
9
Kapininga, John. Manual: Monitoring of malnutrition, diarrhoeal disease & market prices, 1994/95 : final draft (first revision). Blantyre [Malawi]: Council for Nongovernmental Organisations in Malawi, 1995.
Find full text
10
Wittkemper, Hans-Georg. Neuronale Netze als Hilfsmittel zur Rendite- und Risikoschätzung von Aktien. Köln: Botermann & Botermann, 1994.
Find full textBook chapters on the topic "Prices – Data processing"
1
Raudys, Aistis. "Accuracy of MLP Based Data Visualization Used in Oil Prices Forecasting Task." In Image Analysis and Processing – ICIAP 2005, 761–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553595_93.
Full text
2
Steinvorth, Ulrich. "Data Processing and Privacy." In Pride and Authenticity, 185–93. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34117-0_25.
Full text
3
Steinvorth, Ulrich. "Data Processing in Novels." In Pride and Authenticity, 195–98. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34117-0_26.
Full text
4
Demiriz, Ayhan, Ahmet Cihan, and Ufuk Kula. "Analyzing Price Data to Determine Positive and Negative Product Associations." In Neural Information Processing, 846–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10677-4_96.
Full text
5
Desbrosses, Nathalie, and Jacques Girod. "Energy Quantity and Price Data: Collection, Processing and Methods of Analysis." In The Econometrics of Energy Systems, 1–26. London: Palgrave Macmillan UK, 2007. http://dx.doi.org/10.1057/9780230626317_1.
Full text
6
Romanowski, Andrzej, and Michał Skuza. "Towards Predicting Stock Price Moves with Aid of Sentiment Analysis of Twitter Social Network Data and Big Data Processing Environment." In Advances in Business ICT: New Ideas from Ongoing Research, 105–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47208-9_7.
Full text
7
Jakóbczak, Dariusz Jacek. "Data Extrapolation via Curve Modeling in Analyzing Risk." In Natural Language Processing, 1379–407. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0951-7.ch067.
Full textAbstract:
Risk analysis needs suitable methods of data extrapolation and decision making. Proposed method of Hurwitz-Radon Matrices (MHR) can be used in extrapolation and interpolation of curves in the plane. For example quotations from the Stock Exchange, the market prices or rate of a currency form a curve. This chapter contains the way of data anticipation and extrapolation via MHR method and decision making: to buy or not, to sell or not. Proposed method is based on a family of Hurwitz-Radon (HR) matrices. The matrices are skew-symmetric and possess columns composed of orthogonal vectors. The operator of Hurwitz-Radon (OHR), built from these matrices, is described. Two-dimensional data are represented by the set of curve points. It is shown how to create the orthogonal and discrete OHR and how to use it in a process of data foreseeing and extrapolation. MHR method is interpolating and extrapolating the curve point by point without using any formula or function.
8
Mumini, Omisore Olatunji, Fayemiwo Michael Adebisi, Ofoegbu Osita Edward, and Adeniyi Shukurat Abidemi. "Simulation of Stock Prediction System using Artificial Neural Networks." In Deep Learning and Neural Networks, 511–30. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch029.
Full textAbstract:
Stock trading, used to predict the direction of future stock prices, is a dynamic business primarily based on human intuition. This involves analyzing some non-linear fundamental and technical stock variables which are recorded periodically. This study presents the development of an ANN-based prediction model for forecasting closing price in the stock markets. The major steps taken are identification of technical variables used for prediction of stock prices, collection and pre-processing of stock data, and formulation of the ANN-based predictive model. Stock data of periods between 2010 and 2014 were collected from the Nigerian Stock Exchange (NSE) and stored in a database. The data collected were classified into training and test data, where the training data was used to learn non-linear patterns that exist in the dataset; and test data was used to validate the prediction accuracy of the model. Evaluation results obtained from WEKA shows that discrepancies between actual and predicted values are insignificant.
9
Fidan, Neslihan, and Beyza Ahlatcioglu Ozkok. "A Review on Applied Data Mining Techniques to Stock Market Prediction." In Enterprise Business Modeling, Optimization Techniques, and Flexible Information Systems, 108–26. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3946-1.ch009.
Full textAbstract:
A portfolio manager considers forecasting the asset prices and measurement of the market risk of an underlying asset. Financial institutions produce datasets to handle their problems by using data mining tools. Recently new technologies have been developed for tracking, collecting, and processing financial data. From a data analysis point of view, this chapter reviews the published articles based upon predictive data mining applications to stock market index. It is observed that hybrid models that combine data mining techniques or integrate an algorithm to a method work efficiently. Finally, the chapter provides likely directions of future researches.
10
Xu, Shuxiang. "Adaptive Higher Order Neural Network Models and Their Applications in Business." In Artificial Higher Order Neural Networks for Economics and Business, 314–29. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-897-0.ch014.
Full textAbstract:
Business is a diversified field with general areas of specialisation such as accounting, taxation, stock market, and other financial analysis. Artificial Neural Networks (ANN) have been widely used in applications such as bankruptcy prediction, predicting costs, forecasting revenue, forecasting share prices and exchange rates, processing documents and many more. This chapter introduces an Adaptive Higher Order Neural Network (HONN) model and applies the adaptive model in business applications such as simulating and forecasting share prices. This adaptive HONN model offers significant advantages over traditional Standard ANN models such as much reduced network size, faster training, as well as much improved simulation and forecasting errors, due to their ability to better approximate complex, non-smooth, often discontinuous training data sets. The generalisation ability of this HONN model is explored and discussed.
Conference papers on the topic "Prices – Data processing"
1
Han, Zhuoyang, Ang Li, and Yu Sun. "An Automated Data-Driven Prediction of Product Pricing Based on Covid-19 Case Number using Data Mining and Machine Learning." In 9th International Conference on Natural Language Processing (NLP 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101420.
Full textAbstract:
In early 2020, a global outbreak of Corona Disease Virus 2019 (Covid-19) emerged as an acute respiratory infectious Disease with high infectivity and incidence. China imposed a blockade on the worst affected city of Wuhan at the end of January 2020, and over time, covid19 spread rapidly around the world and was designated pandemic by the World Health Organization on March 11. As the epidemic spread, the number of confirmed cases and the number of deaths in countries around the world are changing day by day. Correspondingly, the price of face masks, as important epidemic prevention materials, is also changing with each passing day in international trade. In this project, we used machine learning to solve this problem. The project used python to find algorithms to fit daily confirmed cases in China, daily deaths, daily confirmed cases in the world, and daily deaths in the world, the recorded mask price was used to predict the effect of the number of cases on the mask price. Under such circumstances, the demand for face masks in the international trade market is enormous, and because the epidemic changes from day to day, the prices of face masks fluctuate from day to day and are very unstable. We would like to provide guidance to traders and the general public on the purchase of face masks by forecasting face mask prices.
2
Karasu, Seckin, Aytac Altan, Zehra Sarac, and Rifat Hacioglu. "Prediction of Bitcoin prices with machine learning methods using time series data." In 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404760.
Full text
3
Dos Reis Filho, Ivan José, Guilherme Bittencourt Correa, Guilherme Mendonça Freire, and Solange Oliveira Rezende. "Forecasting future corn and soybean prices: an analysis of the use of textual information to enrich time-series." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/kdmile.2020.11966.
Full textAbstract:
The commodities corn and soybean are products consumed on a large scale in the world. Fluctuations in market prices have far-reaching effects on consumers, farmers, and grain processors. Thus, forecasting the prices of these grains has attracted significant attention from researchers. Forecasting models generally use quantitative time-series data. However, external qualitative factors can influence data in time-series, such as political events, economic crises, and the foreign exchange market. This information is not explicit in the time-series data, and these factors can influence the prediction of the variable values. Textual data extracted from news, forums, and social networks can be a source of knowledge about external factors and potentially useful for time-series forecasting models. Some studies present text mining techniques to combine textual data with time-series. However, the existing representations have some limitations, such as the curse of dimensionality and ineffective attributes. This work applies pre-processing methods in time-series and uses representations combined with textual data to predict the future price of corn and soybeans. The results indicate that the methods used can be an alternative to improve forecasting performance in regression tasks.
4
Wenjuan, Wei, Feng Lu, and Liu Chunchen. "Mixed Causal Structure Discovery with Application to Prescriptive Pricing." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/711.
Full textAbstract:
Prescriptive pricing is one of the most advanced pricing techniques, which derives the optimal price strategy to maximize the future profit/revenue by carrying out a two-stage process, demand modeling and price optimization.Demand modeling tries to reveal price-demand laws by discovering causal relationships among demands, prices, and objective factors, which is the foundation of price optimization.Existing methods either use regression or causal learning for uncovering the price-demand relations, but suffer from pain points in either accuracy/efficiency or mixed data type processing, while all of these are actual requirements in practical pricing scenarios.This paper proposes a novel demand modeling technique for practical usage.Speaking concretely, we propose a new locally consistent information criterion named MIC,and derive MIC-based inference algorithms for an accurate recovery of causal structure on mixed factor space.Experiments on simulate/real datasets show the superiority of our new approach in both price-demand law recovery and demand forecasting, as well as show promising performance in supporting optimal pricing.
5
Arakawa, Masao, Hiroyuki Kitajima, Masahiro Ishida, and Tadaharu Manabe. "Development of Kansei Design System Using Image Processing." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35249.
Full textAbstract:
It is often said that it might be very important to take Kansei, some kind of human sensuous inspiration, into account of design. And it becomes more and more important especially in the design of products whose functions are saturated and it becomes difficult to gain competitive power only by adding a new function. In those cases, it is difficult to gain competitive power by neither discounting its prices nor making efforts in human engineering sense of ease in handling. In such cases, visual design will be more important, so that visual designers become key of success in the product. However, Kansei design used to be based on questionnaires to the customers, and it is basically based on putting emphasis on the greatest common of them. But, in most of cases visual designers would like to show their feelings into design and lead majority to his or her sense. So that Kansei design database is often useless to them and it is sometimes obstacle for their creativity. In this paper, we will propose the Kansei design system which is based on image processing system and qualifying image by using Fourier transform and make up a new visual design by using those Fourier transform data. As an example, we have used the proposed system in design of doors. We have verified the results by inspiration test and brain test and showed a possibility of the proposed method.
6
Mukhamedjanova, Kamola. "Supply Chain Management of Fruits and Vegetables: Realities and Prospects." In International Conference on Eurasian Economies. Eurasian Economists Association, 2018. http://dx.doi.org/10.36880/c10.02114.
Full textAbstract:
Fruit and vegetables are an important sector of agricultural production in Uzbekistan, because they provide the population with sufficient food, as well as income for rural households. Despite this, there are a number of problems in terms of storage, harvesting, processing, and transportation dealing with supply chain management of fruits and vegetables. This article examines the existing mechanism of fruit and vegetables supply chain, as well as offers optimal solutions concerning these issues. In this research there were used secondary data collected from official statistics and professional literatures. As methods of research were used analysis and synthesis, comparison. By practicing improved supply chain management mechanism, there will be significant reduction in the wastages of fruits and vegetables which in turn will benefit both the farmers also the consumers by means of increased returns and decrease in prices respectively.
7
Novotná, Markéta, and Kateřina Hasoňová. "Airbnb jako katalyzátor neudržitelné přeměny měst – případová studie Praha." In XXIII. mezinárodní kolokvium o regionálních vědách / 23rd International Colloquium on Regional Sciences. Brno: Masaryk University Press, 2020. http://dx.doi.org/10.5817/cz.muni.p210-9610-2020-42.
Full textAbstract:
The paper focuses on one of the current global trends related to the expansion of the sharing economy and short-term accommodation services on the Internet platform. The paper aims to evaluate the socio-economic impacts of the Airbnb service in the capital city of Prague, which is one of the most visited cities in Europe. The Airbnb phenomenon, which indirectly contributes to overtourism and the burden on city centres, usually leads to higher house prices, unequal competition, changes in residential areas, and other negative externalities for the residents. The paper applies the desk research method based on the processing of secondary data, supplemented by own observation and in-depth interview with a representative of IPR Prague. When comparing the specific situation and the measures implemented to deal with this problem in other model destinations, serving as a benchmark for the case study, it can be stated that in the case of Prague, the initiative often comes spontaneously from below. The implementation of measures is often carried out only after repeated complaints by residents.
8
Nwulu, Nnamdi I. "A decision trees approach to oil price prediction." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090313.
Full text
9
Lam, K. P., and P. Y. Mok. "Stock price prediction using intraday and AHIPMI data." In 9th International Conference on Neural Information Processing. IEEE, 2002. http://dx.doi.org/10.1109/iconip.2002.1201876.
Full text
10
Tang, Yajuan, Shuang Qiu, and Pengcheng Gui. "Predicting Housing Price Based on Ensemble Learning Algorithm." In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, 2018. http://dx.doi.org/10.1109/idap.2018.8620781.
Full textReports on the topic "Prices – Data processing"
1
Leavy, Michelle B., Danielle Cooke, Sarah Hajjar, Erik Bikelman, Bailey Egan, Diana Clarke, Debbie Gibson, Barbara Casanova, and Richard Gliklich. Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Report on Registry Configuration. Agency for Healthcare Research and Quality (AHRQ), November 2020. http://dx.doi.org/10.23970/ahrqepcregistryoutcome.
Full textAbstract:
Background: Major depressive disorder is a common mental disorder. Many pressing questions regarding depression treatment and outcomes exist, and new, efficient research approaches are necessary to address them. The primary objective of this project is to demonstrate the feasibility and value of capturing the harmonized depression outcome measures in the clinical workflow and submitting these data to different registries. Secondary objectives include demonstrating the feasibility of using these data for patient-centered outcomes research and developing a toolkit to support registries interested in sharing data with external researchers. Methods: The harmonized outcome measures for depression were developed through a multi-stakeholder, consensus-based process supported by AHRQ. For this implementation effort, the PRIME Registry, sponsored by the American Board of Family Medicine, and PsychPRO, sponsored by the American Psychiatric Association, each recruited 10 pilot sites from existing registry sites, added the harmonized measures to the registry platform, and submitted the project for institutional review board review Results: The process of preparing each registry to calculate the harmonized measures produced three major findings. First, some clarifications were necessary to make the harmonized definitions operational. Second, some data necessary for the measures are not routinely captured in structured form (e.g., PHQ-9 item 9, adverse events, suicide ideation and behavior, and mortality data). Finally, capture of the PHQ-9 requires operational and technical modifications. The next phase of this project will focus collection of the baseline and follow-up PHQ-9s, as well as other supporting clinical documentation. In parallel to the data collection process, the project team will examine the feasibility of using natural language processing to extract information on PHQ-9 scores, adverse events, and suicidal behaviors from unstructured data. Conclusion: This pilot project represents the first practical implementation of the harmonized outcome measures for depression. Initial results indicate that it is feasible to calculate the measures within the two patient registries, although some challenges were encountered related to the harmonized definition specifications, the availability of the necessary data, and the clinical workflow for collecting the PHQ-9. The ongoing data collection period, combined with an evaluation of the utility of natural language processing for these measures, will produce more information about the practical challenges, value, and burden of using the harmonized measures in the primary care and mental health setting. These findings will be useful to inform future implementations of the harmonized depression outcome measures.