Готові списки джерел за темами / Price of time

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Добірка наукової літератури з теми "Price of time"

Автор: Grafiati
Опубліковано: 15 лютого 2025

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Статті в журналах з теми "Price of time"

1

Huang, Shaojia, Yisen Zhu, Jingde Huang, Enguang Zhang, and Tao Xu. "Analysis of Circular Price Prediction Strategy for Used Electric Vehicles." Sustainability 16, no. 13 (July 5, 2024): 5761. http://dx.doi.org/10.3390/su16135761.

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As the car price war has intensified in China from 2023, the continuous decline in prices of new cars for both conventional fuel vehicles and electric vehicles (EVs) has led to a sharp decline in used cars. In particular, the EV market appears more vulnerable as the prime cost of battery raw materials has decreased since January 2023. And thus, a second-hand EV price prediction system is urgent. This study compares several methods for used EVs in China. We find that the random forest method and the gradient boosting regression tree (GBRT) method have good effects on predicting used EV prices in respecting price ranges. Timed EV data capture is applied to guarantee the real-time property of our prediction system. Then, we propose the concept of circular pricing, which means that the obsolete data for the priced car will be repriced according to the latest data. In this way, such a system can guide the used car dealers to adjust the price in time.
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2

Ahmadi, Ahmadi, and R. Adisetiawan. "Multivariate Time Series in Macroeconomics." Eksis: Jurnal Ilmiah Ekonomi dan Bisnis 11, no. 2 (November 23, 2020): 151. http://dx.doi.org/10.33087/eksis.v11i2.209.

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Gold is one of the most popular commodities and investment alternatives. Gold prices are thought to be influenced by several other factors such as the US Dollar, oil price, inflation rate, and stock exchange so that gold price modeling is not only influenced by its own value. This research was conducted to determine the best forecasting model and to find out what factors influence the price of gold. This research modeled the price of gold in a multivariate and reviewed the univariate modeling that will be used as a comparison model of multivariate modeling. Univariate modeling is done using ARIMA model where the modeling results state that gold price fluctuations as white noise. Multivariate gold price modeling is done using Vector Error Correction Model with gold, oil, US Dollar and Dow Jones indices, and inflation rate as predictors. The results showed that the VECM model has been able to model the gold price well and all the factors studied influenced the gold price. The US dollar and oil prices are negatively correlated with gold prices, while the inflation rate is positively correlated with gold prices. The Dow Jones index was positively correlated with gold prices in just two periods.
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3

Wang, Diankai, Inna Gryshova, Mykola Kyzym, Tetiana Salashenko, Viktoriia Khaustova, and Maryna Shcherbata. "Electricity Price Instability over Time: Time Series Analysis and Forecasting." Sustainability 14, no. 15 (July 25, 2022): 9081. http://dx.doi.org/10.3390/su14159081.

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Competition in electricity markets leads to volatile conditions which cause persistent price fluctuations over time. This study explores the problem of electricity pricing fluctuations in the DE-LU bidding zone from October 2018 to March 2022 by applying time series analysis. The determinants of electricity price fluctuations are broken down into three groups: exogenous prices (gas, coal and CO2 prices), internal (consumption and generation) and external (net import between neighboring bidding zones) electricity flows. Based on the SARIMAX model, we tried to combine all these factors to forecast electricity prices in the single bidding zone. It was found that the SARIMAX (1, 1, 2) × (3, 1, 0, 7) model with exogenous prices, internal and external electricity flows, which has the lowest AIC and MAPE values, is the best-fitted model for the DE-LU bidding zone. Anonymous trading and unpredictable individual bidding strategies lead to persistent price volatility, which causes electricity prices to deviate from fundamental trends. To reveal the risk factors, the SARIMAX model of electricity prices needs to be supplemented with a GARCH model of the residual returns. For forecasting electricity price residual volatility in the DE-LU bidding zone, the SARIMAX model with exogenous prices, internal and external electricity flows must be accompanied with the GARCH (7, 0) model.
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Kim, Dong-Hwan, and Jin Kim. "Price Prediction Analysis in Seoul APT Market Using Time Series Model." Korea Real Estate Society 71 (March 30, 2024): 193–209. http://dx.doi.org/10.37407/kres.2024.42.1.193.

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The purpose of this study is to make a model to forecasting apartment trade price and household rent price in apartment housing market using ARIMA model. On the basis of those models, I tried to forecast the fluctuations of short-term apartment trade price and household rent price in Seoul apartment market. To analyze the ARIMA model, quarterly data during 2011 1/4∼2023 4/4 are used for identification, estimation, diagnosis, and prediction of the ARIMA model. Using ARIMA model, the outcome ARIMA(1,1,0) model is applied to Sepol apartment trade price in the rate of apartment trade price forecasting model, and ARIMA(1,1,1) model is applied to Seoul apartment household rent price in the rate of apartment rent price. According to the forecast results of Seoul apartment trade price and apartment household rent price in the ARIMA model, looking at the quarterly rate of change in Seoul apartment sales prices, it appears that they will continue to go up the 2024 1/4, and will continue to rise but without significant fluctuations. Looking at the quarterly trend of Seoul apartment rental prices on the 2024 1/4 and continue until the 2024 4/4, and the upward trend will be larger than Seoul apt sale price. While the government's housing policy is sluggish, Seoul apartment sales prices and Jeonse prices are expected to be affected by a decrease in apartment supply and interest rates due to a decrease in permits and occupancy this year.
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5

Curry, David J., and Peter C. Riesz. "Prices and Price/Quality Relationships: A Longitudinal Analysis." Journal of Marketing 52, no. 1 (January 1988): 36–51. http://dx.doi.org/10.1177/002224298805200104.

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Though price and quality are recognized as important tactical and strategic variables for a marketing manager, few empirical data are available on the behavior of price or the correspondence between price and quality over time. The authors report results for three hypotheses derived from product life cycle theory, dynamic pricing policy, and economic information theory about price trends, price convergence, and the correspondence between price and quality among brands in 62 durable product forms. Results strongly confirm the hypotheses that prices converge as well as decrease in real terms. The decline in price variation apparently results from a narrowing of prices by all relevant competitors. Brands entering or exiting a category counterbalance one another and are nearly as likely to be priced below as above a category mean. Reduced correspondence between price and quality levels over time suggests that as pricing flexibility declines, competition may occur in the form of promotional expenditures rather than relative quality improvements. Implications of these findings for marketing strategy and consumer welfare are discussed.
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6

Cheruvu, Sai Manoj. "Stock Price Prediction Using Time Series." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 375–81. http://dx.doi.org/10.22214/ijraset.2021.39296.

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Abstract: Predicting Stock price of a company has been a challenge for analysts due to the fluctuations and its changing nature with respect to time. This paper attempts to predict the stock prices using Time series technique that proposes to observe various changes in a given variable with respect to time and is appropriate for making predictions in financial sector [1] as the stock prices are time variant. Keywords: Stock prices, Analysis, Fluctuations, Prediction, Time series, Time variant
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7

Yao, Jun, and Harmen Oppewal. "Unit pricing matters more when consumers are under time pressure." European Journal of Marketing 50, no. 5/6 (May 9, 2016): 1094–114. http://dx.doi.org/10.1108/ejm-03-2015-0122.

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Purpose This paper aims to first investigate how unit pricing affects consumers’ grocery purchase decisions and perceptions of the shopping task’s information load. The second goal is to test how time pressure enhances the behavioural and perceptual effects of displaying unit prices. Design/methodology/approach Two on-line experiments were conducted using national samples of shoppers. In Study 1, participants indicated their choices and perceptions in an inter-brand shopping scenario where prepackaged products have conflicting positions on retail price and unit price. In Study 2, participants conducted the same shopping task but now under a condition of time pressure. Findings Study 1 shows that unit pricing shifts consumer choices towards the lower unit priced options and improves their perceptions of task information load. Study 2 shows that when consumers are under time pressure, unit pricing shows stronger effects on choices but not on perceptions. Research limitations/implications The study comprised a fairly homogenous set of low involvement categories and relatively small assortments in a hypothetical purchase setting. Exploration of the role of unit pricing in more complex and more realistic purchase environments pose suitable avenues for future research. Practical implications This study shows that consumers benefit from unit pricing because it makes it easier for them to find the lower unit priced items and to more quickly complete their shopping task. Retailers will benefit from increased customer satisfaction and possibly an improved store image. Social implications The study shows that consumers generally benefit from the presence of unit pricing and that unit price information does not create harmful effects in terms of increasing their information load. Originality/value This study uses a specifically designed and controlled but nevertheless realistic grocery choice task to study the effects of unit pricing in an inter-brand context where there are only small differences in size and price. The study contributes to the literature by showing that in such conditions, unit prices help consumers compare the economic losses associated with product options. Their heuristic role is more pronounced when consumers are under time pressure. The study shows that consumers generally benefit from the presence of unit prices.
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8

Trofimov, G. "Competitive Storage and Commodity Price in Continuous Time." Higher School of Economics Economic Journal 26, no. 4 (2022): 523–51. http://dx.doi.org/10.17323/1813-8691-2022-26-4-523-551.

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9

Lee, Yun-Hong. "Effect of Changes in Fertility Rate and Demographic Structure on Housing Prices: Centering on Dongtan New Town." Korean Association for Housing Policy Studies 30, no. 3 (August 31, 2022): 113–39. http://dx.doi.org/10.24957/hsr.2022.30.2.113.

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As a result of analyzing the influence of variables related to the demographic structure, it was found that the price of apartments traded at a time when the total fertility rate was high, the lower the price. In the case of acetic age, the price of apartments traded at a time when the acetic age was higher was found to be lower. It was found that the ratio of single-person households had a negative effect on apartment transaction prices, and the ratio of double-person households had a positive effect on apartment transaction prices. These results suggest that transaction prices tend to fall as apartments traded at a time when the number of single-person households composed of unmarried or students increases. On the contrary, it was found that apartments traded at a time when more double-person households move in are more likely to rise in price. The elderly ratio was found to have no significant effect on apartment transaction prices, but the higher the price of apartments traded at a time when more people in their 60s moved in. In addition, it was found that the high proportion of people in their 60s moved in and apartment prices rose during the period when their transactions were concentrated, mainly in high-priced apartment complexes. These results suggest that changes in fertility rates and demographic structure have a significant impact on apartment prices in new cities. Therefore, it seems important to predict changes in the fertility rate and demographic structure of the region when deciding on apartment supply policies to prevent soaring apartment prices.
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Doucouliagos, Chris. "Price exhaustion and number preference: time and price confluence in Australian stock prices." European Journal of Finance 11, no. 3 (June 2005): 207–21. http://dx.doi.org/10.1080/1351847042000254194.

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Дисертації з теми "Price of time"

1

Yiu, Fu-keung. "Time series analysis of financial index /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18003047.

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2

MALUF, KELLY CRISTINA FERNANDES. "SAZONAL ADJUSTEMENT OF PRICE ÍNDICES TIME SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1998. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8683@1.

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Анотація:
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Esta tese tem como objetivo a comparação entre procedimentos para dessazonalização de séries temporais. As metodologias usadas serão a de Modelos Estruturais Clássicos e Bayesianos e a metodologia padrão de dessazonalização X11 ARIMA. Os dados utilizados são as 35 séries reais de índice de preços ao consumidor - IPC para a Região Metropolitana do Rio de Janeiro, fornecidas pelo Instituto Brasileiro de Geografia e Pesquisa - IBGE, no período de janeiro de 1991 até dezembro de 1997. Os pacotes computacionais utilizados no decorrer do trabalho são FORECAST PRO (X11 ARIMA0, STAMP (Estruturais Clássicos) e BATS (Estruturais Bayesianos). Além disso, foram também utilizadas séries simuladas com sazonalidade, para melhor analisar os resultados desejados.
The aim of this thesis is a comparisson study among three existing procedures for seasonal adjustment of time series, namely: the tradicional X11 ARIMA and those based on the structural model formulation, i.e., the classical approach of A. Harvey and the Bayesian counterpart of Harrison and Stevens. The data used are 25 real time series of Consumer Price Index for Metropolitan area from Rio de Janeiro from 1991 to 1997, supllied by the Instituto Brasileiro de Geografia e Estatística - IBGE. The computacional packages used during the thesis were SPSS and FORECAST PRO (X11 ARIMA), STAMP (structural classical approach) and BATS (structural bayesian approach). Also, simulated seasonal data were to provide a better understanding of the procedures.
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3

Blanck, Andreas. "American Option Price Approximation for Real-Time Clearing." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-144435.

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American-style options are contracts traded on financial markets. These are derivatives of some underlying security or securities that in contrast to European-style options allow their holders to exercise at any point before the contracts expire. However, this advantage aggravates the mathematical formulation of an option's value considerably, explaining why essentially no exact closed-formed pricing formulas exist. Numerous price approximation methods are although available, but their possible areas of application as well as performance, measured by speed and accuracy, differ. A clearing house offering real-time solutions are especially dependent on fast pricing methods to calculate portfolio risk, where accuracy is assumed to be an important factor to guarantee low-discrepancy estimations. Conversely, overly biased risk estimates may worsen a clearing house's ability to manage great losses, endangering the stability of a financial market it operates. The purpose of this project was to find methods with optimal performance and to investigate if price approximation errors induce biases in option portfolios' risk estimates. Regarding performance, a Quasi-Monte Carlo least squares method was found suitable for at least one type of exotic option. Yet none of the analyzed closed-form approximation methods could be assessed as optimal because of their varying strengths, where although the Binomial Tree model performed most consistently. Moreover, the answer to which method entails the best risk estimates remains inconclusive since only one set of parameters was used due to heavy calculations. A larger study involving a broader range of parameter values must therefore be performed in order to answer this reliably. However, it was revealed that large errors in risk estimates are avoided only if American standard options are priced with any of the analyzed methods and not when a faster European formula is employed. Furthermore, those that were analyzed can yield rather different risk estimates, implying that relatively large errors may arise if an inadequate method is applied.
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4

Kwon, Oh-Bok. "A time series analysis on interrelationships among U.S. and Korean livestock prices /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3025631.

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5

Yiu, Fu-keung, and 饒富強. "Time series analysis of financial index." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31267804.

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6

Hisham, Abdelradi Khalaf Fadi Mohamed. "Understanding Recent Food Price Patterns: A Time-Series Approach." Doctoral thesis, Universitat de Barcelona, 2014. http://hdl.handle.net/10803/287226.

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The guiding theme of this thesis is the empirical analysis of recent food price behavior. It is composed of three applied studies that address the impacts of energy prices on both food price levels and volatility, as well as the impact of public information release on futures markets of major agricultural commodities. Non-structural time series econometric techniques are applied for such purpose. In the first chapter, the impact of the Spanish biodiesel industry on agricultural feedstock prices is investigated. Both price level and volatility interactions are evaluated. Three relevant prices are considered: the international crude oil price, the Spanish biodiesel blend price and the Spanish sunflower oil price. Weekly Prices are observed from November 2006 to October 2010, yielding a total of 205 observations. Blended biodiesel, sunflower and crude oil prices are found to be interrelated in the long-run. This parity is preserved by the biodiesel industry in order to be in equilibrium. The impact of biodiesel on sunflower oil price levels is found to be very modest, which is reasonable given the small size of the Spanish biodiesel industry. Volatility spillovers between sunflower and biodiesel markets are found to be significant. Evidence of asymmetries in price volatility patterns is also found, with price declines causing more price instability than price increases. Asymmetries can be triggered by the availability of alternative feedstocks in the market, as well as by the unwillingness of biodiesel producers to increase food prices when feedstocks become more expensive. In the second chapter, the impact of the EU biodiesel market on agricultural feedstock prices is analyzed. The study comprises the period between 06/11/2008 to 14/06/2012, and is based on 189 weekly prices. Cointegration analysis suggests that the three prices have a long-run equilibrium relationship that is preserved by the pure biodiesel price. Biodiesel prices are not found to have an effect on rapeseed oil prices. Volatility of pure biodiesel price is affected by its own past volatility and past pure biodiesel and rapeseed market shocks. Also, evidence is found of asymmetries in price volatility, with negative market shocks having a greater impact than positive ones. While pure biodiesel prices cannot affect rapeseed oil price-levels, they can bring instability to these prices. Inventory building and the euro-dollar exchange rate are found to be relevant risk management instruments that can be used to mitigate the biodiesel and rapeseed oil price volatilities. In the third chapter, the impact of public information in the form of USDA-NASS crop production reports on daily corn and soybeans futures prices is evaluated. The study period is between 1970 to 2004, with a total of 700 observations. Results show that USDA-NASS crop production reports significantly affect futures price levels. Report releases at the beginning and at the end of the harvest season are usually the ones exerting a stronger impact. Report releases are not however found to have an effect on price volatility, which suggests gradual price-level changes as a response to published information. Cross-market effects of news are also found to be significant.
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Agoitia, Hurtado Maria Fernanda del Carmen [Verfasser], and Thorsten [Akademischer Betreuer] Schmidt. "Time-inhomogeneous polynomial processes in electricity spot price models." Freiburg : Universität, 2017. http://d-nb.info/1140735438/34.

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8

Raykhel, Ilya. "Real-time automatic price prediction for eBay online trading /." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2697.pdf.

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Raykhel, Ilya Igorevitch. "Real-Time Automatic Price Prediction for eBay Online Trading." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1631.

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While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of 16%; we have also shown that this application greatly reduces the time a reseller would need to spend on trading activities, since the bulk of market research is now done automatically with the help of the learned model.
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Dickamore, Justin Edward. "Price Slides Within Cattle Markets Over Time and Space." DigitalCommons@USU, 2015. https://digitalcommons.usu.edu/etd/4606.

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The production of cattle in the United State is a very large business. Production begins at the cow-calf level, where a calf is born and raised to a specific weight. This weight is the weaning weight and averages between 300-600 pounds. The calf is then typically shipped to a feedlot, where it is fed a high corn ration which increases the weight of animal quickly and cost effectively to reach a sufficient slaughter weight. Cattle production takes place primarily in 5 different geographical locations which include the North Central, Southeast, Northern Plains, Southern Plains, and West regions. Due to the relationships between fed cattle prices, feeder cattle prices and feed costs, lighter weight feeder cattle typically sell for a higher price per pound than heavier weight feeder cattle. This decrease in price per pound for heavier feeders is often referred to as a feeder cattle price slide. This study is to determine how price slides have reacted over time and space due to the relative changes in fed and feeder cattle prices and the cost of feed. Weekly data was obtained from the Livestock Marketing Information Center (LMIC) on the auction price for feeder cattle at different weights for both steers and heifers. Weekly data on the futures price of live cattle and corn were also obtained from the LMIC. To determine if feeder cattle price slides had changed over time, regression analysis was used to evaluate the relationship between feeder cattle prices at varying weights with the price of fed cattle and the price of corn. Two different time periods were used for the same location: the first period was from 1992 to 1996 and the second period was from 2005 to 2015. Price slides were also examined across space. There were five different geographical locations analyzed: Oklahoma, Nebraska, Georgia, Kansas, and Montana. Each region was regressed individually and then compared. Prices slides were calculated as the difference in the regressed feeder cattle price at each weight. A combination of the time and space was used to evaluate changes in the same model. Results from the regression models returned feeder cattle prices at varying weights for steers and heifers and price slides were calculated from those estimated prices. It was found that price slides are not constant over time and that price slides are geographically specific. In the third objective, it is shown that time and space are both factors in determining price slides for feeder cattle. The implications of this study are to help cattle producers be more aware of market conditions specific to changes in feeding cost. It is also to make aware that price slides are not constant over time and space and therefore, must be reevaluated on a consistent basis.
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Книги з теми "Price of time"

1

Hyerczyk, James A., ed. Pattern, Price & Time. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119198499.

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Hyerczyk, James A. Pattern, Price and Time. New York: John Wiley & Sons, Ltd., 2009.

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3

Biggeri, Luigi, and Guido Ferrari, eds. Price Indexes in Time and Space. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2140-6.

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4

India. Office of the Economic Adviser., ed. Index numbers of wholesale prices in India: A time series presentation, 1971-1986. [New Delhi]: Office of the Economic Adviser, Ministry of Industry, Govt. of India, 1987.

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5

Mahy, E. PRICE S: A debrief report. Manchester: NCC, 1985.

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6

Tōkeikyoku, Japan Sōmushō, ed. Heisei 17-nen kijun shōhisha bukka setsuzoku shisū sōran =: 2005-base linked consumer price index time series. Tōkyō: Sōmushō Tōkeikyoku, 2006.

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7

Tōkeikyoku, Japan Sōmuchō. Heisei 7-nen kijun shōhisha bukka setsuzoku shisū sōran: 1995-base linked consumer price index time series. Tōkyō: Sōmuchō Tōkeikyoku, 1996.

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8

Taipalus, Katja. Detecting asset price bubbles with time-series methods. Helsinki: Finlands Bank, 2012.

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9

Frank, Smets, Vestin David, and European Central Bank, eds. Is time ripe for price level path stability? Frankfurt am Main, Germany: European Central Bank, 2007.

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10

Baeyens, Walter J. RSI: Logic, signals & time frame correlation. Greenville, S.C: Traders Press, 2007.

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Частини книг з теми "Price of time"

1

Brown, Constance. "Price and Time." In Breakthroughs in Technical Analysis, 83–113. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119204749.ch5.

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2

Jarrow, Robert A. "Asset Price Bubbles." In Continuous-Time Asset Pricing Theory, 69–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77821-1_3.

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Jarrow, Robert A. "Asset Price Bubbles." In Continuous-Time Asset Pricing Theory, 75–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74410-6_3.

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4

Olsen, Borgar Tørre. "Component price versus time." In Broadband Access Networks, 87–98. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5795-1_8.

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Zaremba, Adam, and Jacob “Koby” Shemer. "To Time or Not to Time? Tactical Allocation Across Strategies." In Price-Based Investment Strategies, 227–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91530-2_8.

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Brockwell, Peter J. "An Overview of Asset–Price Models." In Handbook of Financial Time Series, 403–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-71297-8_17.

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Deng, Xiaotie, Li-Sha Huang, and Minming Li. "On Walrasian Price of CPU Time." In Lecture Notes in Computer Science, 586–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11533719_60.

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Maurya, Rahul, Dashniet Kaur, Ajay Pal Singh, and Shashi Ranjan. "Stock Price Prediction Using Time Series." In Communications in Computer and Information Science, 309–20. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56700-1_25.

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Antoniadis, I., N. Sariannidis, and S. Kontsas. "The Effect of Bitcoin Prices on US Dollar Index Price." In Advances in Time Series Data Methods in Applied Economic Research, 511–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02194-8_34.

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Carrasco, Raúl, Manuel Vargas, Ismael Soto, Diego Fuentealba, Leonardo Banguera, and Guillermo Fuertes. "Chaotic Time Series for Copper’s Price Forecast." In IFIP Advances in Information and Communication Technology, 278–88. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94541-5_28.

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Тези доповідей конференцій з теми "Price of time"

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Purushotham, K., Bangarappa, Ashwini Kodipalli, and Trupthi Rao. "Real-Time House Price Predictions with Regression Analysis." In 2024 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/raics61201.2024.10689962.

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Luizon, Gustavo, and Bruno Sousa. "RIGGS: Real Time Energy Price in 5G Smart grids." In 2024 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), 1–4. IEEE, 2024. https://doi.org/10.1109/nfv-sdn61811.2024.10807490.

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3

Notaria, Harsh, Shriya Shah, Devarshee Thopte, Hemang Soneji, Pranit Bari, and Khushali Deulkar. "Comparative Analysis of Stock Price Prediction using Time Series Models." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA), 1–6. IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10775112.

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Dugo, Víctor, and David Gávez. "Optimizing floor price in Real Time Bidding." In CARMA 2023 - 5th International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica de València, 2023. http://dx.doi.org/10.4995/carma2023.2023.16452.

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AdNetwork companies are very much a part of today's new digital marketing methods. This paper aims to develop an algorithm that solves the problems of AdNetwork companies in setting optimal floor prices. Establishing the optimal starting price for the bid is equivalent to setting the price that maximises revenue, which is optimal for the publisher and the AdNetwork company. In this market, that price will balance two opposite scenarios: a high floor price could lead to some impressions unsold, while a low floor price could be insufficient to reach profit margins. The contribution is twofold. First, this paper extends the problem of optimal price flor in real time bidding auctions for advertising in current scenarios where a DSP (Demand Side Platform) acts as a filter and only one bid is received by the AdNetwork and thus, the price paid corresponds to the reserve price. and, moreover, it is implemented in reality with a pseudo-algorithm (not provided for commercial reasons). It allowed to be implemented in a real case scenario for three publishers, obtaining an average increase of revenue of 127%.
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Combi, Carlo, Romeo Rizzi, and Pietro Sala. "The Price of Evolution in Temporal Databases." In 2015 22nd International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2015. http://dx.doi.org/10.1109/time.2015.24.

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"Offer Price, Transaction Price and Time-On-Market." In 10th European Real Estate Society Conference: ERES Conference 2003. ERES, 2003. http://dx.doi.org/10.15396/eres2003_118.

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Tahmid Akhand, Md Nafis, Md Ahsan Habib, and Kazi Md Rokibul Alam. "Analyzing Cryptocurrency Price Trends for Real-Time Price Predictions." In 2023 26th International Conference on Computer and Information Technology (ICCIT). IEEE, 2023. http://dx.doi.org/10.1109/iccit60459.2023.10441450.

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Gómez-Losada, Álvaro, and Néstor Duch-Brown. "Some empirical observations on price patterns in online stores." In CARMA 2023 - 5th International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica de València, 2023. http://dx.doi.org/10.4995/carma2023.2023.16505.

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This study aims, through a short experimentation, to empirically identify price patterns in popular products from large online retailers. A set of 35 products and prices were monitored for 15 days, three times per day. Three simple price patterns were identified, and four patterns involving two or more sellers were described. The simple price patterns were Temporary rises and fall of prices, Alternation between two prices, and Ladder steps of prices. Compound pattern prices were Price chasing, Price exchange, Mimic at a lower or similar minimum prices, and Conditioned appearance, most of them described in economic literature. This research does not discuss the use of algorithmic pricing when setting prices by online retailer but it could be involved. Next steps in this research consider to wider the number of analyzed products and to increase the frequency and time of their monitoring.
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Hu, T., C. Chen, and H. Wei. "A Novel Methodology for Forecasting Petrochemical Product Prices in East China Market by Applying ARIMAX Time Series and Machine Learning Models." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23114-ms.

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Анотація:
Abstract Forecasting petrochemical product prices is essential for economic decision making in the petrochemical industry. However, it is a challenging task to achieve accurate forecasts, given the price volatility in East China market, and the fact that the petrochemical product prices can be affected by various factors relevant in the industry. Therefore, we proposed a novel methodology which applied ARIMAX time series and machine learning models, combined with feature selection, for the price forecasting. This paper proposes a novel approach, which involves four steps of data gathering, factor identification, feature selection and model construction, to forecasting the weekly and monthly prices of 24 petrochemical products, ranging from the upstream to the downstream of the petrochemical industrial chain. Among the various relevant factors which might affect the product prices, the most significant ones were identified by applying feature selection. The product prices were modelled and predicted using ARIMAX time series model and various machine learning models, including random forest (RF), support vector machine (SVM), gradient boosted decision tree (GBDT), etc. The data were classified into training set and test set. The results were assessed by mean absolute percentage error (MAPE) - a measure of forecasting accuracy, and direction statistics (Dstat), which evaluates the forecasting performance in terms of a downward/an upward trend in prices. Taking the price forecast of LLDPE in East China market as an example, it was shown by applying feature selection that, among the various relevant factors considered in this paper, the ones affecting LLDPE price the most were brent price, PE futures price and Purchasing Managers’ Index (PMI); additionally, the historical values of LLDPE price were also found to contribute to accurate forecasts. For LLDPE weekly price forecasting, the minimum MAPE of 0.7% was obtained using RF method, with Dstat being 64.1%; and the highest Dstat of 84.2% was achieved by applying GBDT and Multi-Layer Perceptron (MLP) methods, with MAPE being 1.3% and 1.4%, respectively. For LLDPE monthly price forecasting, a MAPE value of 1.3% and a Dstat value of 90.0% were achieved with ARIMAX algorithm. In general, considering all 24 petrochemical products studied in this work, good weekly and monthly forecasts were obtained regarding accuracy and tendency, by applying ARIMAX and machine learning models. The contents in this paper provide the following benefits: first, a wide range of petrochemical products were studied, filling the gaps in the literature and enriching the database; second, the applications of feature selection with a number of machine learning models, as well as ARIMAX model, to price forecasts, were evaluated and the methodology is applicable to other related industries; last but not least, the price forecasts provide guidance for petrochemical production, achieving economical and sustainable industrial development.
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Gayashan, W. A. K., A. K. G. Dayarathna, R. W. M. A. P. Rajakaruna, T. J. N. Perera, and T. S. G. Peiris. "Development of Time Series Model to Predict Daily Gold Price." In SLIIT INTERNATIONAL CONFERENCE ON ADVANCEMENTS IN SCIENCES AND HUMANITIES, 294–300. Faculty of Humanities & Sciences, SLIIT, 2024. https://doi.org/10.54389/wyml9575.

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Gold is ancient and one of the most precious and popular commodities in the world. Gold price forecasting is critical in financial decision-making, providing valuable information for investors in the gold market, sellers of gold items and stakeholders. Not much studies have been carried out in to forecast daily gold prices of Sri Lanka. The aim of this paper is to forecast the daily gold price rate (Rupees/troy ounce) using data from 2nd January 2018 to 14th June 2024 published by the Central Bank of Sri Lanka. The best fitted model was identified as ARIMA (1,1,1) + ARCH (2). The model was trained using data from 2nd January 2018 to 31st May 2024 and validated using data from the 3rd of June 2024 to 14th of June 2024. The model was statistically tested using standard statistical procedure and errors were found as white noise. The Mean Absolute Percentage Error (MAPE) for the training data set and validation data set were 0.748% and 1.002% respectively. The validation confirmed that the ARIMA (1,1,1) + ARCH (2) model effectively captures the dynamics of gold price movements, offering robust predictive power. These results indicate that the model is highly accurate and reliable for forecasting, making it a valuable tool for financial institutions and investors aiming to predict market trends and make informed investment decisions. Keywords: gold price rates; ARIMA models; Forecasting
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Звіти організацій з теми "Price of time"

1

Solórzano, Diego, and Lenin Arango-Castillo. Price Duration Using Daily Online Data: Time- or State-Dependent? Banco de México, August 2024. http://dx.doi.org/10.36095/banxico/di.2024.10.

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Using daily retail prices gathered through web scraping in Mexico, we analyze if price changes can be characterized by time-dependent features, like the duration of the price spell, and/or by variables associated with the state of the economy. Through the lens of a duration model, we find evidence of both time- and state-dependency behavior. Favoring time-dependency, on the one hand, estimates indicate that price spells exhibit greater risk of ending every seven days relative to other days in between. Advocating for state-dependency, the probability of price changes seems to be affected by variations in the USD/MXN exchange rate, variations in real point of sales expenditures and the COVID-19 pandemic. Finally, leveraging data gathered via direct visits to brick-and-mortar stores, we also find time-dependency and state-dependency in the duration of price spells.
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2

Hamermesh, Daniel, and Jeff Biddle. Taking Time Use Seriously: Income, Wages And Price Discrimination. Cambridge, MA: National Bureau of Economic Research, November 2018. http://dx.doi.org/10.3386/w25308.

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Goldberg, Linda, and Christian Grisse. Time Variation in Asset Price Responses to Macro Announcements. Cambridge, MA: National Bureau of Economic Research, October 2013. http://dx.doi.org/10.3386/w19523.

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Rotemberg, Julio. Customer Anger at Price Increases, Time Variation in the Frequency of Price Changes and Monetary Policy. Cambridge, MA: National Bureau of Economic Research, November 2002. http://dx.doi.org/10.3386/w9320.

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Glower, Michel, Donald Haurin, and Patric Hendershott. Selling Price and Selling Time: The Impact of Seller Motivation. Cambridge, MA: National Bureau of Economic Research, March 1995. http://dx.doi.org/10.3386/w5071.

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Graves, Thomas E. It's Time for DoD to Sack Its Price Stabilization Policy,. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada296148.

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Bachmann, Ruediger, Benjamin Born, Steffen Elstner, and Christian Grimme. Time-Varying Business Volatility and the Price Setting of Firms. Cambridge, MA: National Bureau of Economic Research, June 2013. http://dx.doi.org/10.3386/w19180.

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Korajczyk, Robert, Deborah Lucas, and Robert McDonald. Understanding Stock Price Behavior around the Time of Equity Issues. Cambridge, MA: National Bureau of Economic Research, November 1989. http://dx.doi.org/10.3386/w3170.

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Bajari, Patrick, Jane Cooley, Kyoo il Kim, and Christopher Timmins. A Theory-Based Approach to Hedonic Price Regressions with Time-Varying Unobserved Product Attributes: The Price of Pollution. Cambridge, MA: National Bureau of Economic Research, February 2010. http://dx.doi.org/10.3386/w15724.

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Fuster, Andreas, Stephanie Lo, and Paul Willen. The Time-Varying Price of Financial Intermediation in the Mortgage Market. Cambridge, MA: National Bureau of Economic Research, August 2017. http://dx.doi.org/10.3386/w23706.

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