Auto Market Forecast, 1996

Ⅰ. Estimated Macro-economic Environment for 1996 1996 is the first year of the 9th Five-Year Plan, the state will further deepen its system reforms and inject new vitality toward economic development. Economic growth will maintain balanced, inflation will be more

Production Forecast of Citrus in China and Production and Marketing Situation of Citrus in Chongqing in 2016 Production Season

According to the statistics of the Ministry of Agriculture,the planting area of citrus would increase steadily,and the yield would decline slightly,2. 556 7 million ha and 36. 168 million t,respectively. Compared with 2015,the planting area would increase by 1. 97% and the yield would increase by 1. 17%. According to the production scheduling of Chongqing Agricultural Commission,the citrus production in Chongqing in 2016 would continue to maintain a steady and rapid growth,the estimated area and yield were 0. 206 7 million ha and 2. 8 million t,increasing by 4. 27% and 4. 48% compared with 2015 respectively. By the end of November 2016,most of mature citrus products in Chongqing would show different degree of rise in purchasing price,while the purchasing price of red orange and some processed raw material fruits would show different amplitude of decline. On the whole,the production and marketing situation of Chongqing citrus would become better.

A Short-Term Electricity Price Forecasting Scheme for Power Market

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.

ADAPTIVE FORECAST AND CONTROL OF THE MARKET ECONOMIC SYSTEM WITH FUZZY INPUTS

In this paper, the adaptive forecast and control of the market economic system with fuzzy inputs is discussed. A new method which is adapted for the adaptive forecast and control of this kind of system is introduced. Through a living example the better result is explained concretly.

Analysis of Chinese Power Market in 2007 and Its Forecast

Power supply and demand inJanuary-September, 2007Since 2007, the national economy developed continu-ously, showing a situation of rapid growth, more optimizedstructure, increased efficiency and improvement of people'slivelihood. In the first three quarters, GDP achieved 16.6043trillion Yuan, and its year-on-year growth rate was 11.5%;

2004 Neodymia Market and 2005 Forecast

In recent 10 years, global NdFeB magnetic materials industry develops at the increasing speed over 20% every year, which strongly stimulates the fast production improvement of neodymia and neodymium metal. Thereinto, production of Chinese NdFeB enhances the most rapidly. In 2004, output of Chinese sintered NdFeB reached 25,000 tons, up 82.5% over previous year. 1. 2004 Chinese Neodymia Production (1) Production of Southern Ore According to statistics, total 30,000 tons of

Effect of Distributional Assumption on GARCH Model into Shenzhen Stock Market： a Forecasting Evaluation

This paper examines the forecasting performance of different kinds of GARCH model （GRACH, EGARCH, TARCH and APARCH） under the Normal, Student-t and Generalized error distributional assumption. We compare the effect of different distributional assumption on the GARCH models. The data we analyze are the daily stocks indexes for Shenzhen Stock Exchange （SSE） in China from April 3^rd, 1991 to April 14^th, 2005. We find that improvements of the overall estimation are achieved when asymmetric GARCH models are used with student-t distribution and generalized error distribution. Moreover, it is found that TARCH and GARCH models give better forecasting performance than EGARCH and APARCH models. In forecasting performance, the model under normal distribution gives more accurate forecasting performance than non-normal densities and generalized error distributions clearly outperform the student-t densities in case of SSE.

Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.

Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market

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.

ST-Trader:A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement

Stocks that are fundamentally connected with each other tend to move together.Considering such common trends is believed to benefit stock movement forecasting tasks.However,such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data.Motivated by this observation,we propose a framework that incorporates the inter-connection of firms to forecast stock prices.To effectively utilize a large set of fundamental features,we further design a novel pipeline.First,we use variational autoencoder(VAE)to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure(fundamentally clustering).Second,a hybrid model of graph convolutional network and long-short term memory network(GCN-LSTM)with an adjacency graph matrix(learnt from VAE)is proposed for graph-structured stock market forecasting.Experiments on minute-level U.S.stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods.The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.

Survey of feature selection and extraction techniques for stock market prediction

In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price predictions.Several review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market forecasting.However,no survey study has explored feature selection and extraction techniques for stock market forecasting.This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications.We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022.We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles.We also describe the combination of feature analysis techniques and ML methods and evaluate their performance.Moreover,we present other survey articles,stock market input and output data,and analyses based on various factors.We find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

Alternative techniques for forecasting mineral commodity prices

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.

中国成品油市场2023年回顾与2024年供需分析预测

回顾2023年中国成品油市场发展,结合国内经济发展各种因素,对2024年成品油市场进行预测。分析认为:2023年中国成品油需求持续复苏,消费属性突出的汽油和航空煤油(航煤)消费大幅增长,经济增速加快拉动柴油消费温和增长,中国成品油消费量预估为3.66×10^(8)t,同比增长12.2%,为2011年以来再次出现两位数增长;成品油消费税扩围征收提高了调合油的原料成本,减少了低价资源数量;出口配额大幅增加则缓解了国内资源过剩压力。2024年,消费对经济增长的贡献将继续加大,航煤消费将持续快速增长,汽车电动化将导致汽油消费增速大幅放缓,能耗双控逐步转向碳排放双控,柴油消费量将重回下降通道。2024年中国成品油市场仍将呈现供需双增的良好态势,消费量预计为3.69×10^(8)t,较2023年小幅增长0.8%;地方炼厂成品油资源持续增加,国内成品油过剩规模将超过5000×10^(4)t,国内市场竞争形势依然严峻。

The Research on and Application of the Multi-regression Technique in the Course of the Marketing Decision-making of Enterprises

The reformation of the economy system has led the f un ctional department and status of the enterprises into a variable state. Under th e condition of the market economy, the kernel of the enterprises’ functional dep artment has diverted to that of marketing decision-making, which face to market and meet with the need of consumption. Assuredly, the kernel of marketing decis ion-making is to prognosticate the future market demand of the production of en terprises accurately, so that it can ensure and realize the maximum of the enter prises’ profit increase. Using empirical research and the multi-regression technique, this paper ana lyzes the enterprises’ production demand forecast of the GMC (Global Management Challenge, held every year globally) and changes most of uncontrollable factors of demand forecast to the controllable ones of the enterprises. The method we us ed to forecast demand by using the multi-regression technique is as follows: 1. Look for the main factors which influence the demand of productions; 2. Establish the regression model; 3. Using the historical data, find the resolution of the correlative index an d do the prominent test; 4. Analyze and compare, regression, adjust parameter and optimize the regress ion model. Our method will make the forecast data closer to the actual prices of the future market requirement quantity in the production marketing decision-making of the enterprises and realize the optimizing combination and the working object w ith the minimum of the cost and the maximum of the profit. And it can ensure the realization of the equity maximum of the enterprises and increase the lifecycle of the production.

Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

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.

面向电力市场的用户侧电力电量预测综述

对电力电量准确预测,把握其不确定性和随机性对电力市场的管理和发展具有重要意义。该文首先对电力市场及其环境下电力电量预测进行了简要概述;其次,从数据预处理、预测方法和预测场景3个角度出发,对面向电力市场的用户侧电力电量预测研究现状进行了总结与分析,详细阐述了新兴负荷、考虑分布式电源的接入、考虑需求响应、面对特殊事件与极端环境和面对综合能源系统5个场景下用户电力电量预测现状;最后,对现有研究面临的挑战进行了分析,并对未来研究方向进行了展望。

Factors That Affect Consumption Patterns and Market Demands for Honey in the Kingdom of Saudi Arabia

Despite the significant annual consumption of honey in Saudi Arabia, information gaps remain with regard to the marketing and market structure of honey along the value chain. This study analyzed the major factors that influenced the consumption, expenditure patterns, and demand of honey in Saudi Arabia. This study forecasted the near-future expected market demands for honey in Saudi Arabia by collecting and analyzing the primary data using questionnaires. A total of 331 respondents from representative regions and large cities were randomly selected and interviewed. The data were analyzed using qualitative and quantitative methods as well as appropriate econometric models. Respondents characterized honey quality using organoleptic words, and these characterizations varied based on the relative significance of perception parameters. Taste, aroma, physical state, and color had aggregated average scores of 4.58, 4.44, 3.54, and 3.28, respectively. In addition to the above parameters, honey source, brand name, and confidence in the producers influenced its perceived quality. The major outlets for honey in Saudi Arabia included producers, specialized honey stores, and auction markets in major cities during the harvesting seasons. Medication, food, and sweetening were the major motivations for buying honey in the Saudi market, with aggregate scores of 4.52, 3.71, and 1.52, respectively. Significant honey price variations were observed within and among different honeys and packaging volumes;this finding might be due to factors such as botanical and geographical origins, package volume size economics (i.e., bulk purchases), honey variety blending, brand names, and producer policies. The average price of locally produced honey was approximately $73 per kg, which is 10 times more than the average price of honey in the US and the EU. The estimated consumption/income elasticity was 0.27. These results suggest that honey is a basic commodity in Saudi Arabia. Based on econometric model forecasts, the Saudi market demand for honey is expected to reach approximately 29,784 tons in 2025.

An Artificial Neural Network Model to Forecast Exchange Rates

For the purposes of this research, the optimal MLP neural network topology has been designed and tested by means the specific genetic algorithm multi-objective Pareto-Based. The objective of the research is to predict the trend of the ex-change rate Euro/USD up to three days ahead of last data available. The variable of output of the ANN designed is then the daily exchange rate Euro/Dollar and the frequency of data collection of variables of input and the output is daily. By the analysis of the data it is possible to conclude that the ANN model developed can largely predict the trend to three days of exchange rate Euro/USD.

我国蔬菜产业市场运行态势研究

近10年来,我国蔬菜种植面积、产量逐年增加,消费需求也明显上升,总供给和总需求基本平衡,市场运行总体比较稳健。蔬菜市场运行具有季节性波动、产地转换等五大特征。2023年,我国蔬菜市场产销两旺,市场价格高位运行,农业农村部重点监测的28种蔬菜全国批发价格全年平均是近10年来的最高价;展望2024年,蔬菜总供给和总需求基本平衡,略有结余。蔬菜市场主要面临气候变化、种植意愿、产销衔接等五大风险点。建议今后要进一步强化“菜篮子”建设、蔬菜地产地销、均衡上市、监测预警和政策扶持,努力实现蔬菜产业保供稳价和高质量发展。