数量化理论模型在大豆食心虫虫食率的预测中的应用

大豆食心虫的危害程度是由虫食率来决定的,影响大豆食心虫虫食率的因素很多,以往对虫食率的预测方法只是基于定量变量,并且要求预测数据准确,然而影响害虫的危害程度的因素除定量因素外,还有许多定性因素,本文考虑影响大豆食心虫虫食率的定量因素:上年平均脱荚孔数、上年9月(中下旬)平均气温、上年9月份(中下旬)降水量、上年10月份(上旬)降水量、当年7月份平均气温、当年7月份降水量以及当年7月份湿度作自变量和定性因素:大豆品种、幼虫越冬存活率和8月分平均百米蛾量(观测误差较大的数据做为定性变量来考虑),以当年的虫食率作为基准变量建立数量化理论模型,增加了数据资料信息的应用,取得了精确的预测结果,对实际工作有一定的指导意义。     

新疆自然资源与人口发展的互动关系研究——基于相对资源承载力的分析

本文以新疆的历史数据为基础,采用相对自然资源承载力的研究方法,对新疆自然资源与人口发展之间的互动关系进行了深入分析,发现新疆的人口虽还没达到自然资源承载力指数的上限,但是自然资源的相对承载压力度却出现了不断上升的趋势。     

Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.     

The behaviour of betting and currency markets on the night of the EU referendum

We study the behaviours of the Betfair betting market and the sterling/dollar exchange rate (futures price) during 24 June 2016, the night of the EU referendum. We investigate how the two markets responded to the announcement of the voting results by employing a Bayesian updating methodology to update prior opinion about the likelihood of the final outcome of the vote. We then relate the voting model to the real-time evolution of the market-determined prices as the results were announced. We find that, although both markets appear to be inefficient in absorbing the new information contained in the vote outcomes, the betting market seems less inefficient than the FX market. The different rates of convergence to the fundamental value between the two markets lead to highly profitable arbitrage opportunities.     

Information flow between prediction markets,polls and media: Evidence from the 2008 presidential primaries

Are the forecast errors of election-eve polls themselves forecastable? We present evidence from the 2008 Democratic Party nomination race between Barack Obama and Hillary Clinton showing that the answer is yes. Both cross-sectional and time series evidence suggests that market prices contain information about election outcomes that polls taken shortly before the contests do not. Conversely, election surprises relative to polls too Granger cause subsequent price movements. We then investigate whether the additional information in prices could come from the media coverage of these campaigns, and uncover a set of complex relationships between pollster’s surprise, price movements, and various aspects of media coverage. Prices anticipate the balance and content of media coverage, but not the volume. On the other hand, it is the volume of media coverage, not the balance or content, that anticipates the surprise element in election outcomes. Moreover, Granger causality between prices and election surprises barely changes after controlling for media coverage, and causality from media volume to surprises persists too after controlling for price movements. Taken together, the results suggest that both prices and the volume of media coverage contain independent election-relevant information that is not captured in polls.     

The M4 Competition: Results,findings, conclusion and way forward

The M4 competition is the continuation of three previous competitions started more than 45 years ago whose purpose was to learn how to improve forecasting accuracy, and how such learning can be applied to advance the theory and practice of forecasting. The purpose of M4 was to replicate the results of the previous ones and extend them into three directions: First significantly increase the number of series, second include Machine Learning (ML) forecasting methods, and third evaluate both point forecasts and prediction intervals. The five major findings of the M4 Competitions are: 1. Out Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches. 2. The biggest surprise was a “hybrid” approach that utilized both statistical and ML features. This method’s average sMAPE was close to 10% more accurate than the combination benchmark used to compare the submitted methods. 3. The second most accurate method was a combination of seven statistical methods and one ML one, with the weights for the averaging being calculated by a ML algorithm that was trained to minimize the forecasting. 4. The two most accurate methods also achieved an amazing success in specifying the 95% prediction intervals correctly. 5. The six pure ML methods performed poorly, with none of them being more accurate than the combination benchmark and only one being more accurate than Naïve2. This paper presents some initial results of M4, its major findings and a logical conclusion. Finally, it outlines what the authors consider to be the way forward for the field of forecasting.     

Far from the madding crowd: collective wisdom in prediction markets

We investigate market selection and bet pricing in a repeated prediction market model. We derive the conditions for long-run survival of more than one agent (the crowd) and quantify the information content of prevailing prices in the case of fractional Kelly traders with heterogeneous beliefs. It turns out that, apart some non-generic situations, prices do not converge, neither almost surely nor on average, to true probabilities, nor are they always nearer to the truth than the beliefs of all surviving agents. This implies that, in general, prediction market prices are not maximum likelihood estimators of the true probabilities. However, when more than one agent survives, the average price emerging from a prediction market approximates the true probability with lower information loss than any individual belief.     

基于库存理论的铁路始发直达运输产品设计

将库存理论运用到铁路始发直达运输产品设计,能够有效实现运输服务组织成本与库存成本的有机统一。为实现库存成本在铁路始发直达运输中的加载,在考虑周期非完整情况下的库存成本的同时,将铁路运输和库存成本纳入同一目标函数,在装卸车地库存能力约束下,构建基于库存理论的铁路始发直达运输产品设计模型,确定成本最小的铁路始发直达运输产品设计方案。以赤峰地区至山东省煤炭运输网络为例,设计方案有效降低了成本,有助于吸引客户选择铁路运输,以及辅助铁路运营单位研究调整始发直达运输产品方案。     

基于DFNN的金融股指预测及金融非线性系统辨识研究

针对证券市场内部结构的复杂性、外部因素的多变性,本文采用动态模糊神经网络（DFNN）进行金融股指预测。DFNN能够实现在线学习,并且参数估计与结构辨识同时进行;同时采用误差下降率（ERR）修剪技术,保证网络拓扑结构不会持续增长,避免了过拟合及过训练现象,确保了DFNN的泛化能力。本文以上证指数为例．通过与同样以高斯函数作为传递函数的RBF算法预测结果的比较和分析．表明DFNN预测上证指数的偏差较小,预测的方向准确性较高。通过DFNN模型提取的模糊规则对金融系统运行模式进行分析．为研究金融非线性系统辨识提供了启发性思路。     

铁路行包运量预测模型研究

铁路行包运量预测是以运输需求和内部供给为导向,综合考虑各种影响因素,对行包运量现状和发展的正确把握.探讨利用人工神经网络结合主成分分析的方法,建立铁路行包运量预测模型,解释并预测行包专列开行后铁路行包运量的增长趋势.实例分析的仿真结果表明,采用主成分分析法的广义回归神经网络模型结构简洁、预测精度高、收敛速度快,对相关铁路部门和企业的决策具有参考意义.