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Carbon Price Forecasting Approach Based on Multi-Scale Decomposition and Transfer Learning
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作者 Xiaolong Zhang Yadong Dou +2 位作者 Jianbo Mao Wensheng Liu Hao Han 《Journal of Beijing Institute of Technology》 EI CAS 2023年第2期242-255,共14页
Accurate carbon price forecasting is essential to provide the guidance for production and investment.Current research is mainly dependent on plenty of historical samples of carbon prices,which is impractical for the n... Accurate carbon price forecasting is essential to provide the guidance for production and investment.Current research is mainly dependent on plenty of historical samples of carbon prices,which is impractical for the newly launched carbon market due to its short history.Based on the idea of transfer learning,this paper proposes a novel price forecasting model,which utilizes the correlation between the new and mature markets.The model is firstly pretrained on large data of mature market by gated recurrent unit algorithm,and then fine-tuned by the target market samples.An integral framework,including complexity decomposition method for data pre-processing,sample entropy for feature selection,and support vector regression for result post-processing,is provided.In the empirical analysis of new Chinese market,the root mean square error,mean absolute error,mean absolute percentage error,and determination coefficient of the model are 0.529,0.476,0.717%and 0.501 respectively,proving its validity. 展开更多
关键词 carbon emission trading price forecasting transfer learning gated recurrent unit
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Research on Hybrid Model of Garlic Short-term Price Forecasting based on Big Data 被引量:3
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作者 Baojia Wang Pingzeng Liu +5 位作者 Zhang Chao Wang Junmei Weijie Chen Ning Cao Gregory MPO’Hare Fujiang Wen 《Computers, Materials & Continua》 SCIE EI 2018年第11期283-296,共14页
Garlic prices fluctuate dramatically in recent years and it is very difficult to predict garlic prices.The autoregressive integrated moving average(ARIMA)model is currently the most important method for predicting gar... Garlic prices fluctuate dramatically in recent years and it is very difficult to predict garlic prices.The autoregressive integrated moving average(ARIMA)model is currently the most important method for predicting garlic prices.However,the ARIMA model can only predict the linear part of the garlic prices,and cannot predict its nonlinear part.Therefore,it is urgent to adopt a method to analyze the nonlinear characteristics of garlic prices.After comparing the advantages and disadvantages of several major prediction models which used to forecast nonlinear time series,using support vector machine(SVM)model to predict the nonlinear part of garlic prices and establish ARIMA-SVM hybrid forecast model to predict garlic prices.The monthly average price data of garlic in 2010-2017 was used to test the effect of ARIMA model,SVM model and ARIMA-SVM model.The experimental results show that:(1)Garlic price is affected by many factors but the most is the supply and demand relationship;(2)The SVM model has a good effect in dealing with the nonlinear relationship of garlic prices;(3)The ARIMA-SVM hybrid model is better than the single ARIMA model and SVM model on the accuracy of garlic price prediction,it can be used as an effective method to predict the short-term price of garlic. 展开更多
关键词 price forecast machine learning hybrid model GARLIC
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Electricity price forecasting using generalized regression neural network based on principal components analysis 被引量:1
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作者 牛东晓 刘达 邢棉 《Journal of Central South University》 SCIE EI CAS 2008年第S2期316-320,共5页
A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the mai... A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%. 展开更多
关键词 ELECTRICITY price forecasting GENERALIZED regression NEURAL NETWORK principal COMPONENTS analysis
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Alternative techniques for forecasting mineral commodity prices 被引量:1
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作者 C.A.Tapia Cortez S.Saydam +1 位作者 J.Coulton C.Sammut 《International Journal of Mining Science and Technology》 SCIE EI CSCD 2018年第2期309-322,共14页
Forecasting mineral commodity(MC) prices has been an important and difficult task traditionally addressed by econometric, stochastic-Gaussian and time series techniques. None of these techniques has proved suitable to... Forecasting mineral commodity(MC) prices has been an important and difficult task traditionally addressed by econometric, stochastic-Gaussian and time series techniques. None of these techniques has proved suitable to represent the dynamic behavior and time related nature of MC markets. Chaos theory(CT) and machine learning(ML) techniques are able to represent the temporal relationships of variables and their evolution has been used separately to better understand and represent MC markets. CT can determine a system's dynamics in the form of time delay and embedding dimension. However, this information has often been solely used to describe the system's behavior and not for forecasting.Compared to traditional techniques, ML has better performance for forecasting MC prices, due to its capacity for finding patterns governing the system's dynamics. However, the rational nature of economic problems increases concerns regarding the use of hidden patterns for forecasting. Therefore, it is uncertain if variables selected and hidden patterns found by ML can represent the economic rationality.Despite their refined features for representing system dynamics, the separate use of either CT or ML does not provide the expected realistic accuracy. By itself, neither CT nor ML are able to identify the main variables affecting systems, recognize the relation and influence of variables though time, and discover hidden patterns governing systems evolution simultaneously. This paper discusses the necessity to adapt and combine CT and ML to obtain a more realistic representation of MC market behavior to forecast long-term price trends. 展开更多
关键词 price forecasting MINERAL COMMODITY MARKET dynamics CHAOS theory Machine learning
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Return direction forecasting:a conditional autoregressive shape model with beta density
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作者 Haibin Xie Yuying Sun Pengying Fan 《Financial Innovation》 2023年第1期2251-2266,共16页
This paper derives a new decomposition of stock returns using price extremes and proposes a conditional autoregressive shape(CARS)model with beta density to predict the direction of stock returns.The CARS model is con... This paper derives a new decomposition of stock returns using price extremes and proposes a conditional autoregressive shape(CARS)model with beta density to predict the direction of stock returns.The CARS model is continuously valued,which makes it different from binary classification models.An empirical study is performed on the US stock market,and the results show that the predicting power of the CARS model is not only statistically significant but also economically valuable.We also compare the CARS model with the probit model,and the results demonstrate that the proposed CARS model outperforms the probit model for return direction forecasting.The CARS model provides a new framework for return direction forecasting. 展开更多
关键词 Return direction forecasting price extremes CARS Beta distribution
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Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method 被引量:2
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作者 Muhammad Irfan Ali Raza +10 位作者 Faisal Althobiani Nasir Ayub Muhammad Idrees Zain Ali Kashif Rizwan Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Saifur Rahman Omar Alshorman Samar Alqhtani 《Computers, Materials & Continua》 SCIE EI 2022年第9期4249-4265,共17页
In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.... In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant features.To this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the classification.Further,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively. 展开更多
关键词 Smart grid deep neural networks consumer demand big data analytics load forecasting price forecasting
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A Short-Term Electricity Price Forecasting Scheme for Power Market 被引量:1
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作者 Gao Gao Kwoklun Lo +1 位作者 Jianfeng Lu Fulin Fan 《World Journal of Engineering and Technology》 2016年第3期58-65,共8页
Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent t... Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010. 展开更多
关键词 Box-Jenkins Method ARIMA Models Electricity Markets Electricity prices forecasting
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Forecasting Winning Bid Prices in an Online Auction Market - Data Mining Approaches 被引量:1
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作者 KIM Hongil BAEK Seung 《Journal of Electronic Science and Technology of China》 2004年第3期6-11,共6页
To solve information asymmetry problem on online auction, this study suggests and validates a forecasting model of winning bid prices. Especially, it explores the usability of data mining approaches, such as neural ne... To solve information asymmetry problem on online auction, this study suggests and validates a forecasting model of winning bid prices. Especially, it explores the usability of data mining approaches, such as neural network and Bayesian network in building a forecasting model. This research empirically shows that, in forecasting winning bid prices on online auction, data mining techniques have shown better performance than traditional statistical analysis, such as logistic regression and multivariate regression. 展开更多
关键词 Bayesian network data mining neural network price forecasting
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Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference
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作者 Evans Nyasha Chogumaira Takashi Hiyama 《Energy and Power Engineering》 2011年第1期9-16,共8页
This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-tu... This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes. 展开更多
关键词 ELECTRICITY price forecasting SHORT-TERM Load forecasting ELECTRICITY MARKETS Artificial NEURAL Networks Fuzzy LOGIC
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Forecasting Tesla’s Stock Price Using the ARIMA Model 被引量:1
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作者 Qiangwei Weng Ruohan Liu Zheng Tao 《Proceedings of Business and Economic Studies》 2022年第5期38-45,共8页
The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock m... The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy.The autoregressive integrated moving average(ARIMA)model is one of the most widely accepted and used time series forecasting models.Therefore,this paper first compares the return on investment(ROI)of Apple and Tesla,revealing that the ROI of Tesla is much greater than that of Apple,and subsequently focuses on ARIMA model’s prediction on the available time series data,thus concluding that the ARIMA model is better than the Naïve method in predicting the change in Tesla’s stock price trend. 展开更多
关键词 Stock price forecast ARIMA model Naïve method TESLA
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Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market
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作者 Gao Gao Kwoklun Lo Fulin Fan 《Energy and Power Engineering》 2017年第4期120-126,共7页
In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper intr... In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model. 展开更多
关键词 ELECTRICITY MARKETS ELECTRICITY priceS ARIMA MODELS ANN MODELS Short-Term forecasting
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A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets
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作者 Nitin Singh S. R. Mohanty 《Journal of Power and Energy Engineering》 2015年第9期1-19,共19页
In deregulated electricity markets, price forecasting is gaining importance between various market players in the power in order to adjust their bids in the day-ahead electricity markets and maximize their profits. El... In deregulated electricity markets, price forecasting is gaining importance between various market players in the power in order to adjust their bids in the day-ahead electricity markets and maximize their profits. Electricity price is volatile but non random in nature making it possible to identify the patterns based on the historical data and forecast. An accurate price forecasting method is an important factor for the market players as it enables them to decide their bidding strategy to maximize profits. Various models have been developed over a period of time which can be broadly classified into two types of models that are mainly used for Electricity Price forecasting are: 1) Time series models;and 2) Simulation based models;time series models are widely used among the two, for day ahead forecasting. The presented work summarizes the influencing factors that affect the price behavior and various established forecasting models based on time series analysis, such as Linear regression based models, nonlinear heuristics based models and other simulation based models. 展开更多
关键词 ELECTRICITY price forecasting Time Series Models ARIMA GARCH ANN Fuzzy ARTMAP
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Pig Price Fluctuations and Forecasting Model Based on Information Platform
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作者 Xi ZHOU 《Asian Agricultural Research》 2016年第9期16-19,共4页
Pork is common in people's daily life consumption,and it accounts for more than half of all meats. By collecting data information published by Bureau of Statistics and Bureau of Agriculture,this paper makes a stat... Pork is common in people's daily life consumption,and it accounts for more than half of all meats. By collecting data information published by Bureau of Statistics and Bureau of Agriculture,this paper makes a statistical analysis of the influence of price fluctuation in the pork market on China's pork production,and finds that China's pork production shows a general trend of fluctuations due to the impact of price factors.According to the predecessors' studies on the factors influencing pig market price,combined with the actual situation of pig breeding in China,this paper uses the latest website data released by the government's public information platform to establish a forecasting model. 展开更多
关键词 Pig price fluctuations Information platform forecasting model
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SVR-Boosting ensemble model for electricity price forecasting in electric power market
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作者 周佃民 高琳 +1 位作者 管晓宏 高峰 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2008年第1期90-94,共5页
A revised support vector regression (SVR) ensemble model based on boosting algorithm (SVR-Boosting) is presented in this paper for electricity price forecasting in electric power market. In the light of characteristic... A revised support vector regression (SVR) ensemble model based on boosting algorithm (SVR-Boosting) is presented in this paper for electricity price forecasting in electric power market. In the light of characteristics of electricity price sequence, a new triangular-shaped 为oss function is constructed in the training of the forecasting model to inhibit the learning from abnormal data in electricity price sequence. The results from actual data indicate that, compared with the single support vector regression model, the proposed SVR-Boosting ensemble model is able to enhance the stability of the model output remarkably, acquire higher predicting accuracy, and possess comparatively satisfactory generalization capability. 展开更多
关键词 electricity price forecasting support vector regression boosting algorithm ensemble model gen-eralization capability
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Energy Price Forecasting Through Novel Fuzzy Type-1 Membership Functions
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作者 Muhammad Hamza Azam Mohd Hilmi Hasan +2 位作者 Azlinda A Malik Saima Hassan Said Jadid Abdulkadir 《Computers, Materials & Continua》 SCIE EI 2022年第10期1799-1815,共17页
Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future prices.Electricity price forecasting have been a critical input to ... Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future prices.Electricity price forecasting have been a critical input to energy corporations’strategic decision-making systems over the last 15 years.Many strategies have been utilized for price forecasting in the past,however Artificial Intelligence Techniques(Fuzzy Logic and ANN)have proven to be more efficient than traditional techniques(Regression and Time Series).Fuzzy logic is an approach that uses membership functions(MF)and fuzzy inference model to forecast future electricity prices.Fuzzy c-means(FCM)is one of the popular clustering approach for generating fuzzy membership functions.However,the fuzzy c-means algorithm is limited to producing only one type of MFs,Gaussian MF.The generation of various fuzzy membership functions is critical since it allows for more efficient and optimal problem solutions.As a result,for the best and most improved results for electricity price forecasting,an approach to generate multiple type-1 fuzzy MFs using FCM algorithm is required.Therefore,the objective of this paper is to propose an approach for generating type-1 fuzzy triangular and trapezoidal MFs using FCM algorithm to overcome the limitations of the FCM algorithm.The approach is used to compute and improve forecasting accuracy for electricity prices,where Australian Energy Market Operator(AEMO)data is used.The results show that the proposed approach of using FCM to generate type-1 fuzzy MFs is effective and can be adopted. 展开更多
关键词 Fuzzy logic fuzzy C-means type-1 fuzzy membership function electricity price forecasting
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Forecasting on Crude Palm Oil Prices Using Artificial Intelligence Approaches
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作者 Abdul Aziz Karia Imbarine Bujang Ismail Ahmad 《American Journal of Operations Research》 2013年第2期259-267,共9页
An accurate prediction of crude palm oil (CPO) prices is important especially when investors deal with ever-increasing risks and uncertainties in the future. Therefore, the applicability of the forecasting approaches ... An accurate prediction of crude palm oil (CPO) prices is important especially when investors deal with ever-increasing risks and uncertainties in the future. Therefore, the applicability of the forecasting approaches in predicting the CPO prices is becoming the matter into concerns. In this study, two artificial intelligence approaches, has been used namely artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). We employed in-sample forecasting on daily free-on-board CPO prices in Malaysia and the series data stretching from a period of January first, 2004 to the end of December 2011. The predictability power of the artificial intelligence approaches was also made in regard with the statistical forecasting approach such as the autoregressive fractionally integrated moving average (ARFIMA) model. The general findings demonstrated that the ANN model is superior compared to the ANFIS and ARFIMA models in predicting the CPO prices. 展开更多
关键词 CRUDE PALM Oil priceS NEURO Fuzzy NEURAL Networks Fractionally Integrated forecast
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Estimation Methodology of Short-term Natural Rubber Price Forecasting Models
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作者 A.A. Khin M. Zainalabidin +2 位作者 S. Mad Nasir E.C.F. Chong A. Fatimah Mohamed 《Journal of Environmental Science and Engineering》 2011年第4期460-474,共15页
This study developed a short-term econometric model of world natural rubber price Standard Malaysia Rubber Grade 20 (SMR20). Both single and simultaneous equations were utilized using monthly data from January 1990-... This study developed a short-term econometric model of world natural rubber price Standard Malaysia Rubber Grade 20 (SMR20). Both single and simultaneous equations were utilized using monthly data from January 1990-December 2008 as estimation period and data from January 2009-June 2009 was used as an ex-ante forecast. The data were tested for unit root and Vector Error Correction and co-integration method was used to estimate the parameters of the model. The models specifications were developed in order to discover the inter-relationships between NR production, consumption and prices of SMR20 and to determine forecast price of SMR20. Comparative analysis between the single-equation specification and simultaneous supply-demand and price equation were made in terms of their estimation accuracy based on RMSE, MAE and (U-Thile) criteria. Ex-ante forecasts was carried out for the period of January 2009-June 2009. The results revealed that the values of the RMSE, MAE and U of simultaneous supply-demand and price equations model were comparatively smaller than the values generated by the single-equation model. These statistics suggest that the simultaneous equation of supply-demand and price model is more accurate and efficient measure in terms of its statistical criteria than the single-equation model in predicting the price of SMR20 in the next 6 months. 展开更多
关键词 Supply-demand and price model econometric forecasting single equation natural rubber price
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A Look-Ahead Method for Forecasting the Concrete Price
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作者 Qing Liu Minghao Huang Woon-Seek Lee 《Journal of Applied Mathematics and Physics》 2022年第5期1859-1871,共13页
Price movement of building materials increases the uncertainty of architectural planning. As a basic building material, commercial concrete is an important part of various construction costs. It is of great significan... Price movement of building materials increases the uncertainty of architectural planning. As a basic building material, commercial concrete is an important part of various construction costs. It is of great significance to predict its price change trend in advance. In this paper, a univariate autoregressive series is constructed based on the daily average price of concrete in major cities in China;then it uses a combined model of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) to extract the spatial and temporal rules of time series, to achieve accurate prediction of the trend of concrete price changes 10 days ago. The prediction accuracy rate of the model is 97.13%, and the precision, recall rate, and F1 score are: 97.15%, 97.27%, and 97.20%, respectively. The prediction result is of great significance to various architectural planning. 展开更多
关键词 price forecasting CONCRETE Deep Learning AUTOREGRESSION
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Stock Price Forecasting with Artificial Neural Networks Long Short-Term Memory: A Bibliometric Analysis and Systematic Literature Review
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作者 Cristiane Orquisa Fantin Eli Hadad 《Journal of Computer and Communications》 2022年第12期29-50,共22页
This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks—RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock p... This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks—RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock price projection. Through bibliometric analysis and systematic literature review, it is observed that 333 authors wrote on the topic between 2018 and March 2022, and the journals Expert Systems with Applications, IEEE Access, Big Data Journal and Neural Computing and Applications, published the most relevant articles. Of the 99 articles published in this period, 43 are associated with Chinese institutions, the most cited being that of Kim and Won, who studies the volatility of returns and the market capitalization of South Korean stocks. The basis of 65% of the studies is the comparison between the RNN LSTM and other artificial neural networks. The daily closing price of shares is the most analyzed type of data, and the American (21%) and Chinese (20%) stock exchanges are the most studied. 57% of the studies include improvements to existing neural network models and 42% new projection models. 展开更多
关键词 Stock price forecasting Long-Term Memory Backpropagation Bibliometric Analysis Systematic Review
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Short-Term and Long-Term Price Forecasting Models for the Future Exchange of Mongolian Natural Sea Buckthorn Market
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作者 Yalalt Dandar Liu Chang 《Agricultural Sciences》 2022年第3期467-490,共24页
Sea buckthorn market floated uncertainly within a narrow range. The market situation provided upward pressure on prices, and producer and consumer interest were poor, coupled with weak prices in the regional markets. ... Sea buckthorn market floated uncertainly within a narrow range. The market situation provided upward pressure on prices, and producer and consumer interest were poor, coupled with weak prices in the regional markets. The objectives of the study are: 1) to estimate the relationship between wild Sea buckthorn (SB) price and Supply, Demand, while some other factors of crude oil price and exchange rate by using simultaneous Supply-Demand and Price system equation and Vector Error Correction Method (VECM);2) to forecast the short-term and long-term SB price;3) to compare and evaluate the price forecasting models. Firstly, the data was analyzed by Ferris and Engle-Granger’s procedure;secondly, both price forecasting methodologies were tested by Pindyck-Rubinfeld and Makridakis’s procedure. The result shows that the VECM model is more efficient using yearly data;a short-term price forecast decreases, and a long-term price forecast is predicted to increase the Mongolian Sea buckthorn market. 展开更多
关键词 Short-Term and Long-Term price forecasting Models Simultaneous System Equation VECM Sea Buckthorn Mongolia
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