Relevant bibliographies by topics / Forecasting prices, natural rubber market

Contents

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

Academic literature on the topic 'Forecasting prices, natural rubber market'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Forecasting prices, natural rubber market.'

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 "Forecasting prices, natural rubber market"

1

Pham, Nhien T. "Application of SARIMA model to forecasting the natural rubber price in the world market." Journal of Agriculture and Development 17, no. 06 (December 31, 2018): 1–7. http://dx.doi.org/10.52997/jad.1.03.2018.

Full text
Abstract:
This study was conducted to develop a forecasting model to predict the price natural rubber in the world market by using the Seasonal Autoregressive Integrated Moving Average (SARIMA). The dataset for model development was collected from series data of average monthly closing average prices in the natural rubber - Ribbed Smoked Sheet No.3 (RSS3) on the Tokyo Commodity Exchange (TOCOM) for the period of January 2007 - September 2018. The RSS3 price on the TOCOM provided the reference price for natural rubber in the world market. It resulted SARIMA(2,1,2)(1,1,1)12 model was selected as the best-fit model. The model achieved 0.000 for Probability value (P-value). 8.86 for Akaike Information Criterion (AIC) and 9.01 for Schwarz Information Criterion (SIC); 6.68% for Mean Absolute Percentage Error (MAPE) and 21.43 for Root Mean Square Error (RMSE). This model was used to forecast the world's natural rubber price during October 2018 - December 2020. This study may be helpful to the farmers, traders, and the governments of the world's important natural rubber producing countries to plan policies to reduce natural rubber production costs and stabilize the natural rubber price in the future, such as by setting suitable areas for natural rubber plantation in each country and defining appropriate and sustainable alternative crop areas in each country.
APA, Harvard, Vancouver, ISO, and other styles
2

Bubshait, Abdulaziz K. "Butadiene Rubber in the Petrochemical Industry." International Annals of Science 11, no. 1 (December 31, 2021): 22–26. http://dx.doi.org/10.21467/ias.11.1.22-26.

Full text
Abstract:
The Butadiene is a raw material used in the petrochemical industry. The use of Butadiene has risen with petrochemical market growth. The Global market is forecasting a demand growth for butadiene applications, especially for rubber materials. The estimated synthetic rubber market is $19.1 billion in 2021 and forecasted to reach $23.2 billion in five years. The dynamic growth in butadiene applications will introduce new products used in many things from the food industry to sports and goods. Also, the rubber materials have different applications in the automotive industry, oil and gas, medical products, and plastics. Companies’ strategic planning to increase the production of synthetic rubber for the global market. The demand increased as new applications were introduced to the market. The stability of oil prices will have the rubber market steady which always leads to optimal pricing. The diver for Butadiene rubber applications is to maximize production by having different kind of materials that applied for several products. The global business development indicated the ability to increases the synthetic rubber market rubber and capacities, which will enhance the chemical process techniques, new technology design, and efficiency that will maximize production and minimize product cost. Looking into the price difference between synthetic and natural rubber, many fluctuation variables were introduced in the price of each type. For example, synthetic rubber price is high, depending on crude oil, natural gasoline and naphtha prices, since those feedstocks are fed to the cracking units, as C4 is one of the cracking products. Therefore, any change in the oil prices will influence the butadiene price, which is the feed for most rubber plants. In addition, the utilities required for those plants to operate have a major impact on overall price. On the other hand, Natural rubber is an agricultural product and dependent on soil type and weather.
APA, Harvard, Vancouver, ISO, and other styles
3

MATHEW, SHYJU, and RAMASAMY MURUGESAN. "Indian natural rubber price forecast–An Autoregressive Integrated Moving Average (ARIMA) approach." Indian Journal of Agricultural Sciences 90, no. 2 (November 15, 2022): 418–22. http://dx.doi.org/10.56093/ijas.v90i2.103067.

Full text
Abstract:
The objective of this study was to forecast the price of natural rubber in India during April 2019 to March 2020 by employing autoregressive integrated moving average (ARIMA). The monthly pricing data for the period from April 2008 to March 2018 was used for the study. The analysis was carried out during the year 2018–19. RSS4 (Ribbed Smoked Sheets), latex (60% DRC (Dry Rubber Content)) and ISNR 20 (Indian Standard Natural Rubber) are the different types of Indian natural rubber that are competitive in international rubber market. The prices of these types of natural rubber were taken for modelling. AIC was used as a selection criterion for the best-fitted model. ARIMA(3,1,2) for RSS 4, ARIMA (3,1,2) for Latex 60% DRC, and ARIMA (4,1,3) for ISNR20were the most suited modelsto forecast the price.The evaluation metrics were R2, Adjusted R2, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). These were employed for validating the forecasting model. The price forecasting of natural rubber in India can be a better-suited tool for the policymakers to decide on their investment in natural rubber cultivation.
APA, Harvard, Vancouver, ISO, and other styles
4

Cheong Fu, Mong, and Shariffah Suhaila Syed Jamaludin. "Forecasting Malaysia Bulk Latex Prices Using Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing." Malaysian Journal of Fundamental and Applied Sciences 18, no. 1 (February 28, 2022): 70–81. http://dx.doi.org/10.11113/mjfas.v18n1.2404.

Full text
Abstract:
Natural rubber is an important component of many developed countries' socioeconomic structures because it is frequently used to manufacture essential consumer goods such as tires and latex gloves. The natural rubber industry is heavily affected by the volatility and unpredictability of the natural bulk latex markets. Forecasting natural rubber prices is critical for rubber industry in procurement decisions and marketing strategies. This study aims to model monthly bulk latex prices in Malaysia using Autoregressive Integrated Moving Averages (ARIMA) and Exponential Smoothing. The models performance are measured using the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The Malaysian Rubber Board has 132 historical prices for the latex in Malaysia from January 2010 to December 2020. They are used for training and testing in determining the forecasting accuracy. Overall finding show that ARIMA (1,1,0) provides the most accurate prediction. With a MAPE of 8.59 percent and an RMSE of 69.78 sen per kilogram, this model is considered the best and highly accurate.
APA, Harvard, Vancouver, ISO, and other styles
5

Mohamad Norizan, Nor Farah Hanim Binti, and Zahayu Binti Md Yusof. "Forecasting Natural Rubber Price in Malaysia by 2030." Malaysian Journal of Social Sciences and Humanities (MJSSH) 6, no. 9 (September 10, 2021): 382–90. http://dx.doi.org/10.47405/mjssh.v6i9.986.

Full text
Abstract:
Natural rubber (NR) has recently become one of Malaysia's most important economic sectors. Despite, the price of Standard Malaysia Rubber 20 changes frequently. That is why it is important to develop a NR price forecasting model. Because there was a significant time lag between making output decisions and the actual output of the commodity in the market. The aim of this study is to determine the time series pattern for natural rubber price in Malaysia within 1995 until 2020 and to forecast the natural rubber price in Malaysia for 10 years ahead. The data used is from year 1995 until 2020 that were obtained from Malaysian Rubber Board (MRB). This study also used univariate forecasting like Naïve with Trend, Double Exponential Smoothing, Holt’s Winter and Autoregressive Integrated Moving Average (ARIMA). Then, the measurement error is used to determine the best method to forecast the future data. The measurement error that used in this study are Mean Absolute Error, Mean Squared Error, Root Mean Square Error, Mean Absolute Percentage Error and The Theil Inequality Coefficient. Result: The natural rubber price in Malaysia showed a trend pattern. Then, ARIMA is used to determine the forecast of natural rubber price for next 10 years since it has the lowest measurement error. Conclusion: There are volatility in the price of natural rubber in Malaysia over the next 10 years.
APA, Harvard, Vancouver, ISO, and other styles
6

Khin, Aye Aye, Seethaletchumy Thambiah, and Kevin Low Lock Teng. "Short-term and long-term price forecasting models for the future exchange of Malaysian natural rubber market." International Journal of Agricultural Resources, Governance and Ecology 13, no. 1 (2017): 21. http://dx.doi.org/10.1504/ijarge.2017.084032.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Erni, Nofi, M. Syamsul Maarif, Nastiti S.Indrasti, Machfud Machfud, and Soeharto Honggokusumo. "Model Prakiraan Harga dan Permintaan pada Rantai Pasok Karet Spesifikasi Teknis Menggunakan Jaringan Syaraf Tiruan." JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI 1, no. 3 (April 4, 2012): 116. http://dx.doi.org/10.36722/sst.v1i3.49.

Full text
Abstract:
<p style="text-align: justify;">Karet spesifikasi teknis (TSR) merupakan jenis karet alam yang penting, dengan pertumbuhan permintaan yang tinggi dibanding jenis karet alam yang diproduksi dan diekspor oleh Indonesia. TSR paling banyak digunakan sebagai bahan baku untuk industri ban, sehingga dengan tumbuhnya indutri otomotif mendorong peningkatan permintaan terhadap TSR. Namun permasalahan muncul dalam produksi TSR, dimana tingkat fluktuasi baik karena kelebihan maupun kekurangan produksi sangat berpengaruh terhadap perubahan harga TSR di pasar Internasional. Untuk mengurangi fluktuasi tersebut diperlukan suatu metode untuk memperkirakan tingkat permintaan dan harga. Penelitian ini bertujuan untuk merancang suatu metode prakiraan yang dapat merperkirakan tingkat harga dan volume permintaan untuk TSR 20. Prakiraan dilakukan dengan Jaringan Syaraf Tiruan (JST) dengan algoritma propagasi balik, menggunakan data perkembangan pasar TSR di bursa berjangka SICOM. Model JST yang dirancang mempertimbangkan pola harga, pola permintaan dan interaksi kedua faktor. Hasil simulasi menunjukkan penggunaan 5 input neuron yaitu: 1) harga tertinggi, 2) harga terendah, 3) harga penutupan, 4) volume permintaan awal, 5) volume permintaan penutupan, 15 neuron pada lapisan tersembunyi dan 2 output yaitu harga dan volume permintaan pada lapisan output. Tingkat akurasi hasil prakiraan harga mencapai 91% dan akurasi prakiraan permintaan 87%. Berdasarkan hasil prakiraan ditentukan status harga dan permintaan. Harga tinggi jika perbedaan antara nilai maksimum dan nilai tengah lebih tinggi dari 47%, harga rendah jika perbedaan antara nilai minimum dan nilai tengah lebih dari 20%. Prakiraan permintaan dinyatakan tinggi atau rendah jika terjadi peningkatan maupun penurunan sebesar 50 % dari rata-rata permintaan<em>.</em></p><h6 style="text-align: center;"><strong><em>Abstract</em> </strong></h6><p style="text-align: justify;">Technically Specified Rubber (TSR) is the most important of natural rubber type which has a high demand growth which is produced and exported by Indonesia. TSR is mostly used as raw material for tire industries, as the world’s automotive industries grow up the demand for TSR is also rise up. However, the problem appears in the production of TSR, which is fluctuative production rate in the form of over and under production correlated to the price change in International market. Therefore, a method to forecast the price and demand level is needed to design in order to reduce fluctuation. The result is a forecasting that used as an input for preparing and adjusting TSR rubber production planning that working adaptively with market condition by utilising the expert knowledge. This research aimed to design a method that can forecast the changes in price level and demand volume. Artificial Neural Network (ANN) which is backpropagation algorithm that has been designed according to data TSR market condition in SICOM is used in this research, the ANN model is modified by observing the price pattern, demand pattern and the connection between both of them together. Experiments have shown that the optimal architecture network for price and demand forecasting can be obtained by using 5 different neuron parameter, there are: 1) the highest price, 2) the lowest price, 3) the closing price, 4) demand volume interest, 5) demand volume close for input layer, 15 neuron for hidden layer and 2 different neuron there are price and demand volume for output layer. The accuracy of forecasting price had reached 91% and 87% for forecasting demand. Based on forecasting result had determined the state of price and demand. The price is high if the differences between maximum and mean score is higher than 47% and the price is low if the differences between the minimum and mean score is higher than 20%. The demand is high if the demand forecasting is higher than 50% and it is low if smaller than 50% of average demand volume.</p>
APA, Harvard, Vancouver, ISO, and other styles
8

Pramananda, Penggawa Pietra, Amzul Rifin, and Dahlia Nauly. "The Effect of Domestic Consumption on Natural Rubber Farmgate Price in Indonesia." AGRARIS: Journal of Agribusiness and Rural Development Research 8, no. 2 (December 28, 2022): 248–60. http://dx.doi.org/10.18196/agraris.v8i2.12480.

Full text
Abstract:
The decline in natural rubber farmgate prices in recent years directly impacted Indonesia’s natural rubber market. In response to this phenomenon, the government plans to increase natural rubber domestic consumption to raise Indonesia’s rubber price. This study aimed to determine the effect of increasing natural rubber domestic consumption on natural rubber farmgate prices and analyze other factors those influence it. The Error Correction Model was used to identify the variables that significantly affect Indonesia’s natural rubber farmgate price. The data used in this study were monthly data from January 2012 to December 2017. Results showed that natural rubber domestic consumption did not significantly affect the Indonesia natural rubber farmgate price. However, in the long run, Indonesia’s natural rubber farmgate price was influenced by the previous period of Indonesia’s natural rubber prices, world natural rubber prices, world crude oil prices, and exchange rates. While in the short run, Indonesia’s natural rubber farmgate price was influenced by the previous period of Indonesia’s natural rubber prices, world natural rubber prices, and exchange rate.
APA, Harvard, Vancouver, ISO, and other styles
9

Su, Chi-Wei, Lu Liu, Ran Tao, and Oana-Ramona Lobonţ. "Do natural rubber price bubbles occur?" Agricultural Economics (Zemědělská ekonomika) 65, No. 2 (February 27, 2019): 67–73. http://dx.doi.org/10.17221/151/2018-agricecon.

Full text
Abstract:
In this paper, we employ the Generalized Supremum Augmented Dickey-Fuller test in order to identify the existence of multiple bubbles in natural rubber. This approach is practical for the using of time series and identifies the beginning and end points of multiple bubbles. The results reveal that there are five bubbles, where exist the divergences between natural rubber prices and their basic values on account of market fundamentals. The five bubbles are related to imbalance between supply and demand, inefficiencies of smallholders market, oil prices, exchange rate and climatic changes through analyses. Thus, the corresponding authorities are supposed to identify bubbles and consider their evolutions, which is beneficial to the stability of natural rubber price.
APA, Harvard, Vancouver, ISO, and other styles
10

Rani, Velpula Jhansi. "Forecasting the Prices of Indian Natural Rubber using ARIMA Model." International Journal of Pure & Applied Bioscience 6, no. 2 (March 28, 2018): 217–21. http://dx.doi.org/10.18782/2320-7051.5464.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Forecasting prices, natural rubber market"

1

Lim, Jit Yang. "An evaluation of alternative forecasting models for natural rubber prices." Thesis, Curtin University, 2002. http://hdl.handle.net/20.500.11937/1731.

Full text
Abstract:
One of the prominent features of the Natural Rubber (NR) market is its price variability, and the aim of this study is to project accurate short-term NR prices. This is accomplished by exploiting the use of forecasting techniques and information sets to seek the combination with the best forecasts, and exploring best ways of combining forecasts. We evaluate the relative performance of 19 models based upon three different forecasting techniques, and four information sets. In addition, we compare their forecasts with 13 other forecasts combined in various different ways, and taking the Naive forecast as benchmark. The generalised autoregressive conditional heteroscedasticity regression (or ARCH-type) models, though more complex, are generally better than the simpler regression models. In general, the performance of the various techniques seems to perform consistently well (or poorly) over the forecasting horizons, with alternations in performance due mainly to the type of information set used. We also adopted a simple trading rule to find out the economic values of our forecasts, and the results are most promising. Importantly, the forecasts generated from the alternative models developed in this study can potentially be beneficial to participants in the NR futures market.
APA, Harvard, Vancouver, ISO, and other styles
2

Lim, Jit Yang. "An evaluation of alternative forecasting models for natural rubber prices." Curtin University of Technology, School of Economics and Finance, 2002. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=13249.

Full text
Abstract:
One of the prominent features of the Natural Rubber (NR) market is its price variability, and the aim of this study is to project accurate short-term NR prices. This is accomplished by exploiting the use of forecasting techniques and information sets to seek the combination with the best forecasts, and exploring best ways of combining forecasts. We evaluate the relative performance of 19 models based upon three different forecasting techniques, and four information sets. In addition, we compare their forecasts with 13 other forecasts combined in various different ways, and taking the Naive forecast as benchmark. The generalised autoregressive conditional heteroscedasticity regression (or ARCH-type) models, though more complex, are generally better than the simpler regression models. In general, the performance of the various techniques seems to perform consistently well (or poorly) over the forecasting horizons, with alternations in performance due mainly to the type of information set used. We also adopted a simple trading rule to find out the economic values of our forecasts, and the results are most promising. Importantly, the forecasts generated from the alternative models developed in this study can potentially be beneficial to participants in the NR futures market.
APA, Harvard, Vancouver, ISO, and other styles
3

Sohail, Tariq. "Developing market sentiment indicators for commodity price forecasting using machine learning." 2017. http://hdl.handle.net/1993/32038.

Full text
Abstract:
The objective of this study is to develop a market sentiment model for financial markets using machine learning, and to illustrate these methods using commodity price data. A market sentiment model may capture the fundamental and crowd psychology of the market, through a variable that uses positive and negative words and phrases. The commodity price used is the daily price of the spot crude oil exchange-traded fund (ETF), United States Oil Fund (USO). The forecasting power of the market sentiment model is compared with a traditional autoregressive model. The results showed that the autoregressive models did not have significant forecasting power for the oil data over the time period examined and the addition of the sentiment model did not improve the forecasting power. Machine learning is a relatively new forecasting method. Therefore, further research on this topic is needed before any firm conclusions can be drawn regarding the effectiveness of this approach.
February 2017
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Forecasting prices, natural rubber market"

1

Zant, Wouter. Stockholding, price stabilization and futures trading: Some empirical investigations of the Indian natural rubber market. Utrecht, The Netherlands: International Books, 1998.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

California Energy Commission. Public Interest Energy Research. Analysis of California natural gas market, supply infrastructure, regulatory implications, and future market conditions: PIER final project report. Sacramento, Calif.]: California Energy Commission, 2009.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Board, Canada National Energy. Natural gas market assessment: Price convergence in North American natural gas markets. Calgary, Alta: National Energy Board, 1995.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Brathwaite, Leon D. 2012 natural gas market trends: In support of the 2012 Integrated Energy Policy Report Update : staff report. [Sacramento]: California Energy Commission, 2012.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Board, Canada National Energy. Natural gas market assessment: Natural gas supply Western Canada, recent developments (1982-92), short-term deliverability outlook (1993-1996). Ottawa: The Board, 1993.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Zant, Wouter. Stockholding, Price Stabilization and Futures Trading: Some Empirical Investigations of the Indian Natural Rubber Market. International Books, 1999.

Find full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Forecasting prices, natural rubber market"

1

Thrall, Grant Ian. "Housing and Residential Communities." In Business Geography and New Real Estate Market Analysis. Oxford University Press, 2002. http://dx.doi.org/10.1093/oso/9780195076363.003.0009.

Full text
Abstract:
Housing occupies about 70 percent of the land area of a typical city. That land area is not randomly distributed, but instead follows regular spatial patterns; these patterns are sectorial and radial (see Hoyt 1939; chapter 2). These geographic patterns form housing submarkets. Specific demographic groups are attracted to housing in those submarkets. As there are many kinds of demographic characteristics of households, there are also many types of housing, and many housing submarkets. Housing submarkets include downtowns, middle-burbs, suburbs; high income; middle income, and low income; new development, mixed use, older development, and mixed new infill with older development; apartments, condominiums; townhouses, high rises, and single-family dwellings. The market analyst makes recommendations on which type of development will be most successful in which submarket and on which submarket would be appropriate for a particular type of development (see Sumichrast and Seldin 1977). Few people today choose to live without the benefit of some type of housing. The choice and availability of what type of housing to live in depends on a complex interaction of many factors, including culture, the natural and built environment, technological scale of society, government, income, stage of life cycle, economics of building construction, and knowledge and imagination of those building the housing. This chapter presents a broad overview of housing market analysis. In the overview, the determinants to demand and supply of housing are presented (See also Harvey, 1992). There is a broad overview of forecasting procedures and methodologies, the methods for projecting absorption rate, housing demand, and competitive supply, and how sales prices and rental prices might be determined. In the last quarter of the nineteenth century, upper-middle-income urban households in the United States and Canada often lived in what are today commonly referred to as Victorian houses. These houses were designed for multigenerational living, including grandparents as the head of household, their children, and their grandchildren. Aunts, uncles, and cousins might have lived in the same dwelling. All the family subunits contributed to the finances of maintaining the house. This provided social security to the elder members of the household, and inexpensive yet high-quality living conditions for the other family members.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Forecasting prices, natural rubber market"

1

Erandi, J. D. T., L. H. K. Dilshan, K. N. T. Piyasena, and N. V. Chandrasekara. "Time Series and Neural Network Approaches for Accurate Forecasting of Monthly Natural Rubber Prices in Sri Lanka." In 2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI). IEEE, 2022. http://dx.doi.org/10.1109/slaai-icai56923.2022.10002480.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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 text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Forecasting prices, natural rubber market' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography