Prediction Model of Weekly Retail Price for Eggs Based on Chaotic Neural Network

This paper establishes a short-term prediction model of weekly retail prices for eggs based on chaotic neural network with the weekly retail prices of eggs from January 2008 to December 2012 in China.In the process of determining the structure of the chaotic neural network,the number of input layer nodes of the network is calculated by reconstructing phase space and computing its saturated embedding dimension,and then the number of hidden layer nodes is estimated by trial and error.Finally,this model is applied to predict the retail prices of eggs and compared with ARIMA.The result shows that the chaotic neural network has better nonlinear fitting ability and higher precision in the prediction of weekly retail price of eggs.The empirical result also shows that the chaotic neural network can be widely used in the field of short-term prediction of agricultural prices.

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.

Short-Term and Long-Term Price Forecasting Models for the Future Exchange of Mongolian Natural Sea Buckthorn Market

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.

Forecast on Price of Agricultural Futures in China Based on ARIMA Model

The forecast on price of agricultural futures is studied in this paper. We use the ARIMA model to estimate the price trends of agricultural futures,which can help the investors to optimize their investing plans. The soybean future contracts are taken as an example to simulate the forecast based on the auto-regression coefficient(p),differential times(d) and moving average coefficient(q). The results show that ARIMA model is better to simulate and forecast the trend of closing prices of soybean futures contract,and it is applicable to forecasting the price of agricultural futures.

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.

China futures price forecasting based on online search and information transfer

The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications.This study combines data from the Baidu index(BDI),Google trends(GT),and transfer entropy(TE)to forecast a wide range of futures prices with a focus on China.A forecasting model based on a hybrid gray wolf optimizer(GWO),convolutional neural network(CNN),and long short-term memory(LSTM)is developed.First,Baidu and Google dual-platform search data were selected and constructed as Internetbased consumer price index(ICPI)using principal component analysis.Second,TE is used to quantify the information between online behavior and futures markets.Finally,the effective Internet-based consumer price index(ICPI)and TE are introduced into the GWO-CNN-LSTM model to forecast the daily prices of corn,soybean,polyvinyl chloride(PVC),egg,and rebar futures.The results show that the GWO-CNN-LSTM model has a significant improvement in predicting future prices.Internet-based CPI built on Baidu and Google platforms has a high degree of real-time performance and reduces the platform and language bias of the search data.Our proposed framework can provide predictive decision support for government leaders,market investors,and production activities.

Hybrid Network Model Based on Data Enhancement for Short-term Power Prediction of New PV Plants

This study proposes a hybrid network model based on data enhancement to address the problem of low accuracy in photovoltaic(PV)power prediction that arises due to insuffi cient data samples for new PV plants.First,a time-series gener ative adversarial network(TimeGAN)is used to learn the distri bution law of the original PV data samples and the temporal correlations between their features,and these are then used to generate new samples to enhance the training set.Subsequently,a hybrid network model that fuses bi-directional long-short term memory(BiLSTM)network with attention mechanism(AM)in the framework of deep&cross network(DCN)is con structed to effectively extract deep information from the origi nal features while enhancing the impact of important informa tion on the prediction results.Finally,the hyperparameters in the hybrid network model are optimized using the whale optimi zation algorithm(WOA),which prevents the network model from falling into a local optimum and gives the best prediction results.The simulation results show that after data enhance ment by TimeGAN,the hybrid prediction model proposed in this paper can effectively improve the accuracy of short-term PV power prediction and has wide applicability.

Maximizing Supermarket Profits:Data-Driven Strategies for Pricing,Sales,and Forecasting

The actual circumstances of daily life are crucial for the purchasing and pricing strategies of supermarkets.Developing strategies based on these circumstances can assist businesses in ensuring profits and fostering win-win cooperation.This paper explores methods to maximize profit through purchasing and sales strategies.Initially,the relevant data for various categories of vegetables is integrated.Through histograms,their sales patterns are directly understood,highlighting the most popular vegetables.Upon analyzing each vegetable category,it becomes evident that their sales data do not conform to normal distributions.Therefore,Spearman correlation coefficients are calculated,revealing strong correlations between certain categories,such as aquatic roots and edible fungi.A line chart depicting the top ten selling vegetables indicates a noticeable periodicity.Traditional fitting methods struggle to adequately model the sales of each vegetable category and their relationship with cost-plus pricing.To address this,additional factors such as holidays,weeks,and months are incorporated using techniques like random forest regression.This approach yields cost-plus pricing dependence curves that better capture the relationship,while effectively managing noise.Regarding sales volume prediction,the original data displays significant volatility,necessitating the handling of outliers using the threshold method.For missing data,linear interpolation is employed to mitigate the impact of continuous missing values on prediction accuracy.Subsequently,Adam-optimized long short-term memory(LSTM)networks are utilized to forecast incoming quantities for the next seven days.By extrapolating from normal sales volume,market capacity is estimated,allowing for additional sales through discount strategies.This framework has the potential to increase original income by 1.1 times.

改进的人工智能神经网络预测模型及其应用

针对传统人工智能预测算法在对预测问题峰值变化处理问题上的不足,引入峰值识别理论改进BP神经网络预测模型(SIBP)。在此基础上,利用引入多向全局搜索机制的改进粒子群算法,对SIBP神经网络预测方法进行改进,提出一种具有峰值识别能力、全局学习能力更强的人工智能预测模型,以有效解决基于BP学习方法易于陷入局部极值的问题。将改进后的预测方法应用于"尖峰突变"比较突出的出清电价预测问题,以美国PJM电力市场2005-02-01至2005-05-16的实际数据为样本,对所提出的改进预测方法进行实证分析。研究结果表明:所提出的算法较改进前的BP算法对发生电价突变的短期电价预测精度提高10.16%,运算时间仅增加6.2 s,预测结果证明本文所提出的算法在处理峰值预测问题方面的有效性。

电力市场中基于混沌理论的电价预测研究

随着电力市场化的不断深入,边际电价预测的重要性将愈来愈凸显,已成为广大发电企业普遍关注的焦点。如何开展边际电价预测以及如何提高边际电价预测的准确性成为迫切需要解决的问题。结合混沌理论,演绎出新的电价预测方法。

定比回归法在价格指数预测中的应用

为了解决各类统计方法在预测价格指数时精度普遍不高的问题 ,利用定比回归的思想 ,给出价格指数的一种具有较好精度和直接经济背景的预测方法 ,并在价格异常波动的年份通过修正回归直线的斜率的方式来得到修正的预测值 ,使人们在各种情况下都可以对价格指数的走向有一个较为明确的了解。这种方法 ,在预测各类增长指数型的指标时 。

城镇综合用地宗地地价评估方法研究

根据我国目前的地价体系,提出了可以运用基准地价分类修正综合法、剩余法和灰色预测法等方法相结合来评估综合用地宗地地价,并模拟了一个评估实例,认为将这几种方法相结合进行综合用地宗地的评估是切实可行的。

Short-Term Electricity Price Forecasting Using Random Forest Model with Parameters Tuned by Grey Wolf Algorithm Optimization

Accurately forecasting short-term electricity prices is of great significance to electricity market participants.Compared with the time series forecasting methods,machine learning forecasting methods can consider more external factors.The forecasting accuracy of machine learning models is greatly affected by the parameters,meanwhile,the manual selection of parameters usually cannot guarantee the accuracy and stability of the forecasting.Therefore,this paper proposes a random forest(RF)electricity price forecasting model based on the grey wolf optimizer(GWO)to improve the accuracy of forecasting.Among them,RF has a good ability to deal with the problem of non-linear and unstable electricity prices.The optimization of model parameters by GWO can overcome the instability of the forecasting accuracy of manually tune parameters.On this basis,the short-term electricity prices of the PJM power market in four seasons are separately predicted.Experimental results show that the RF algorithm can better predict the short-term electricity price,and the optimization of the RF forecasting model by GWO can effectively improve the accuracy of the RF forecasting model.

青岛市区土地与住房价格关系及其对策研究

我国房地产市场快速发展,房价迅速飙升,超出了广大群众的承受范围,严重影响着群众的生活质量,以及整个社会的和谐。该文充分利用所掌握的青岛市近年来房地产行业的大量翔实的原始数据,客观真实地揭示土地价格与住房价格二者的关系,以便为国家房地产市场调控提供可靠的科学依据,促进我国房地产市场行业发展。

Short-term Scheduling of Steam Power System in Iron and Steel Industry under Time-of-use Power Price

A generalized formulation for short-term scheduling of steam power system in iron and steel industry under the time-of-use（TOU）power price was presented,with minimization of total operational cost including fuel cost,equipment maintenance cost and the charge of exchange power with main grid.The model took into account the varying nature of surplus byproduct gas flows,several practical technical constraints and the impact of TOU power price.All major types of utility equipments,involving boilers,steam turbines,combined heat and power（CHP）units,and waste heat and energy recovery generators（WHERG）,were separately modeled using thermodynamic balance equations and regression method.In order to solve this complex nonlinear optimization model,a new improved particle swarm optimization（IPSO）algorithm was proposed by incorporating time-variant parameters,a selfadaptive mutation scheme and efficient constraint handling strategies.Finally,a case study for a real industrial example was used for illustrating the model and validating the effectiveness of the proposed approach.

粮食最低收购价的合理定价模型

为分析粮食最低收购价是否具有合理性,以2006年~2015年小麦最低收购价相关数据为样本,建立超效率DEA模型,得到小麦最低收购价的合理定价范围为每公斤1.36元~2.50元。同时根据超效率DEA模型的超效率值,对"十二五"期间国家公布的小麦最低收购价进行了合理性评价。基于影响小麦最低收购价的6项指标数据,利用Matlab软件,分别建立Elman神经网络预测模型和灰色预测GM(1,1)模型,得出了2017年小麦最低收购价的合理范围。

基于机器学习的智能TWAP和VWAP算法的研究及应用

TWAP与VWAP算法为两类较常见的经典交易算法.传统的VWAP算法在TWAP算法的基础上,大多使用预测日内成交量分布的方法指导算法下单.传统成交量分布的预测效果严重依赖于市场交易惯性,但交易量分布受到日内诸多突发因素的影响,导致算法对市场突发状况的应对能力较弱.本文对传统TWAP与VWAP算法进行改进,利用滚动的1分钟粒度高频实时资金博弈数据,基于Logistic分类器训练量价模型,以该预测结果为入参构建最优化期望执行均价模型,求出当下各个价格档位对应委托数量的最优解.通过相对高频的分钟级价格预测机制,保证算法实时跟踪市场行情走势并寻求相对优势的交易机会.该算法经测试可以稳定地跑赢市场均价,具备推广应用的可行性.

Flexible electricity price forecasting by switching mother wavelets based onwavelet transform and Long Short-Term Memory

Under dynamic pricing, stable and accurate electricity price forecasting on the demand side is essential forefficient energy management. We have developed a new electricity price forecasting model that providesconsistently accurate forecasts. The base prediction model decomposes the time series using wavelet transformand then predicts it by Long Short-Term Memory. Previous studies using this model have always decomposedtime series in the same way without changing the mother wavelet. However, this makes it difficult to respond tochanges in time series that vary daily or seasonally. Therefore, we periodically switch the mother wavelet, i.e.,flexibly change the time series decomposition method, to achieve stable and highly accurate electricity priceforecasting. In an experiment, the model improved prediction accuracy by up to 42.8% compared to predictionwith a fixed mother wavelet. Experimental results show that the proposed flexible forecasting method canconsistently provide highly accurate forecasts.

《聊斋志异·白秋练》中白秋练形象来源考证

《聊斋志异·白秋练》中的白秋练性情风雅,喜好吟诗,吟诗不仅能治愈相思病,甚至让她死而不朽,白秋练更是凭借自己能预知物价的本领成功与慕蟾宫结为夫妻。本文考察了白秋练这一形象的来源,其喜好吟诗的特点,与蒲松龄和好友顾青霞之间的经历密切相关,白秋练通过诗歌进行占卜、治病、表达感情,体现了诗歌的功能;白秋练拥有"术知物价"的本领,帮助慕蟾宫家经商盈利,这在唐传奇、明清小说中也有类似的故事情节;白秋练这一美好的女性形象,也体现了蒲松龄的审美理想。

Prediction of Shanghai Stock Index Based on Investor Sentiment and CNN-LSTM Model

In view of the breakthrough progress of the depth learning method in image and other fields,this paper attempts to introduce the depth learning method into stock price forecasting to provide investors with reasonable investment suggestions.This paper proposes a stock prediction hybrid model named ISI-CNN-LSTM considering investor sentiment based on the combination of long short-term memory(LSTM) and convolutional neural network(CNN).The model adopts an end-to-end network structure,using LSTM to extract the temporal features in the data and CNN to mine the deep features in the data can effectively improve the prediction ability of the model by increasing investor sentiment in the network structure.The empirical part makes a comparative experimental analysis based on Shanghai stock index in China.By comparing the experimental prediction results and evaluation indicators,it verifies the prediction effectiveness and feasibility of ISI-CNN-LSTM network model.