Price Prediction of Seasonal Items Using Time Series Analysis

The price prediction task is a well-studied problem due to its impact on the business domain.There are several research studies that have been conducted to predict the future price of items by capturing the patterns of price change,but there is very limited work to study the price prediction of seasonal goods(e.g.,Christmas gifts).Seasonal items’prices have different patterns than normal items;this can be linked to the offers and discounted prices of seasonal items.This lack of research studies motivates the current work to investigate the problem of seasonal items’prices as a time series task.We proposed utilizing two different approaches to address this problem,namely,1)machine learning(ML)-based models and 2)deep learning(DL)-based models.Thus,this research tuned a set of well-known predictive models on a real-life dataset.Those models are ensemble learning-based models,random forest,Ridge,Lasso,and Linear regression.Moreover,two new DL architectures based on gated recurrent unit(GRU)and long short-term memory(LSTM)models are proposed.Then,the performance of the utilized ensemble learning and classic ML models are compared against the proposed two DL architectures on different accuracy metrics,where the evaluation includes both numerical and visual comparisons of the examined models.The obtained results show that the ensemble learning models outperformed the classic machine learning-based models(e.g.,linear regression and random forest)and the DL-based models.

The Causes of Hiking Ethiopian Consumer Prices

A growing consumer price is creating instability in the macroeconomic environment and hinders the consumption level of especially the poor society.This paper then explored the major causes of such increasing consumer prices using Ethiopian cases.Using data from the National Bank of Ethiopia from 1982/1983 to 2019/2020,it condensed the information of monetary sector,external sector and fiscal sector variables to a small set to estimate the causes of Ethiopian consumer price hiking using the ARDL model.The factors determining consumer price differ from food to non-food.The most important factors determining food price are price expectation and fiscal factors.On the other hand,the main determinant of non-food consumer prices is the fiscal factor.The author also found evidence of fiscal factors and price expectation effects on general consumer prices.Therefore,to contain the rise in consumer prices,it needs to exercise conservative fiscal stances,which require minimizing deficit financing,reducing the import tax rate and reducing domestic indirect tax rates such as excise tax and value added tax on basic consumer goods and services.Moreover,sound government policies are essential to address inflation anticipations(providing information for society about the future of inflation)to change public opinion.

The interactions between Chinese local corn and WTI crude oil prices:an empirical analysis

This paper investigates the relationship between China’s fuel ethanol promotion plan and food security based on the interactions between the crude oil market, the fuel ethanol market and the grain market. Based on the US West Texas Intermediate(WTI) crude oil spot price and Chinese corn prices from January 2008 to May 2018, this paper applies Granger causality testing and a generalized impulse response function to explore the relationship between world crude oil prices and Chinese corn prices. The results show that crude oil prices are not the Granger cause of China’s corn prices, but changes in world crude oil prices will have a long-term positive impact on Chinese corn prices. Therefore, the Chinese government should pay attention to changes in crude oil prices when promoting fuel ethanol. Considering the conduction e ect between fuel ethanol and the food market, the government should also take some measures to ensure food security.

Electricity price forecasting using generalized regression neural network based on principal components analysis

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%.

Competitive analysis of price online inventory problem with cost function

In this paper,a new price is given to the online decision maker at the beginning of each day.The trader must decide how many items to purchase according to the current price.We present three variants and an online algorithm based on cost function.The competitive ratio of the online algorithm is given for each variant,which is a performance measure of an online algorithm.More importantly,we show that the online algorithm is optimal.

Spatial Quantitative Analysis of Garlic Price Data Based on ArcGIS Technology

In order to solve the hidden regional relationship among garlic prices,this paper carries out spatial quantitative analysis of garlic price data based on ArcGIS technology.The specific analysis process is to collect prices of garlic market from 2015 to 2017 in different regions of Shandong Province,using the Moran's Index to obtain monthly Moran indicators are positive,so as to analyze the overall positive relationship between garlic prices;then using the geostatistical analysis tool in ArcGIS to draw a spatial distribution Grid diagram,it was found that the price of garlic has a significant geographical agglomeration phenomenon and showed a multi-center distribution trend.The results showed that the agglomeration centers are Jining,Dongying,Qingdao,and Yantai.At the end of the article,according to the research results,constructive suggestions were made for the regulation of garlic price.Using Moran’s Index and geostatistical analysis tools to analyze the data of garlic price,which made up for the lack of position correlation in the traditional analysis methods and more intuitively and effectively reflected the trend of garlic price from low to high from west to east in Shandong Province and showed a pattern of circular distribution.

Urban natural gas demand and factors analysis in China:Perspectives of price and income elasticities

Urban natural gas is becoming the main sector driving China’s natural gas consumption growth in recent years.This study explores the impacts of urban natural gas price,wage,socioeconomic determinants,and meteorological conditions on urban natural gas demand in China over 2006-2017.Furthermore,this study also analyzes the potential regional heterogeneity and asymmetry in the impacts of gas price and income on China’s urban gas demand.Empirical results reveal that:(1)The increased gas price can significantly reduce the urban gas demand,and the average income level may effectively promote the gas demand,also,a strong switching effect exists between electricity and natural gas in urban China;(2)these impacts are heterogeneous in regions among China,urban natural gas demand is largely affected by the gas price in regions with high-gas-price and by income in regions with low-gas-price;and(3)the impact of gas price on urban gas consumption is consistent in regions with different urban natural gas consumption,while the impact of income is asymmetric.This study further provides several policy implications for improving the urban natural gas industry in China.

Competitive analysis of online inventory problem with interrelated prices

This paper investigates the online inventory problem with interrelated prices in which a decision of when and how much to replenish must be made in an online fashion even without concrete knowledge of future prices. Four new online models with different price corre- lations are proposed in this paper, which are the linear-decrease model, the log-decrease model, the logarithmic model and the exponential model. For the first two models, the online algo- rithms are developed, and as the performance measure of online algorithm, the upper and lower bounds of competitive ratios of the algorithms are derived respectively. For the exponential and logarithmic models, the online algorithms are proposed by the solution of linear programming and the corresponding competitive ratios are analyzed, respectively. Additionally, the algorithm designed for the exponential model is optimal, and the algorithm for the logarithmic model is optimal only under some certain conditions. Moreover, some numerical examples illustrate that the algorithms based on the dprice-conservative strategy are more suitable when the purchase price fluctuates relatively flat.

Electricity Price Influence Factors Analysis Using Stochastic Matrix for Real-Time Electricity Price Forecasting

Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.

Comparative Analysis of the Purchase Price of Raw Milk in the World

This paper selects 20 countries from the major dairy producing continents such as Oceania,the Americas,Europe and Asia,for the comparative analysis of the purchase price of raw milk in the world. Based on the summarization of general features of the world raw milk prices,this paper elaborates the fluctuations in the purchase price of raw milk in Oceania,the Americas,Europe and Asia,respectively,and carries out the comparative study of the gap between the domestic purchase price of raw milk and the world purchase price of raw milk.

The price analysis of Chinese high-speed economic growth

Accelerating economic development in various countries today is the common demands. In the past 20 years, China created an economic development miracle, but also highlighted the depletion of resources, the deterioration of ecological, unfair distribution, the income gap and other social issues. The article analyses the causes of the price and the countermeasures.

Stock Price Forecasting with Artificial Neural Networks Long Short-Term Memory: A Bibliometric Analysis and Systematic Literature Review

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.

Correlation Analysis of Fiscal Revenue and Housing Sales Price Based on Multiple Linear Regression Model

This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis and regression analysis to comprehensively study the correlation between financial revenue and housing sales price in China,and establishes the relationship between financial revenue and housing sales price When the average selling price of commercial housing increases by one unit,the fiscal revenue will increase by 27.855 points.

基于改进Morgenstern-Price法的露天矿山边坡稳定性分析

为解决传统Morgenstern-Price法未考虑岩体抗剪强度参数离散性的问题,引入BartonBandis准则对Morgenstern-Price法进行改进,考虑了岩体结构面抗剪强度参数JRC,JCS,φr的离散性,充分呈现了岩体结构面特征参数的真实统计分布特征,并利用此方法对边坡稳定性进行了分析;而后结合改进的Rosenblueth法对边坡的破坏概率进行了计算。以凤田矿山为背景,利用改进的Morgenstern-Price法计算得到稳定性系数为1.013,说明凤田矿山处于欠稳定状态;其结合改进的Rosenblueth法得到边坡破坏概率为38.759%,说明凤田矿山处于中等危险状态。通过与其他方法的评价结果对比发现,改进的Morgenstern-Price法既能用于计算边坡稳定性系数,又能与改进的Rosenblueth法相结合计算边坡破坏概率,是一种方便、有效的边坡稳定性分析方法,可满足矿山边坡现场施工的要求。

Hadoop + Spark Platform Based on Big Data System Design of Agricultural Product Price Analysis and Prediction by HoltWinters

In the market of agricultural products, the price of agricultural products is affected by production cost, market supply and other factors. In order to obtain the market information of agricultural products, the price fluctuation can be analyzed and predicted. A distributed big data software platform based on Hadoop, Hive and Spark is proposed to analyze and forecast agricultural price data. Firstly, Hadoop, Hive and Spark big data frameworks were built to store the data information of agricultural products crawled into MYSQL. Secondly, the information of agricultural products crawled from MYSQL was exported to a text file, uploaded to HDFS, and mapped to spark SQL database. The data was cleaned and improved by Holt-Winters (three times exponential smoothing method) model to predict the price of agricultural products in the future. The data cleaned by spark SQL was imported and predicted by improved Holt-Winters into MYSQL database. The technologies of pringMVC, Ajax and Echarts were used to visualize the data.

Analysis of an Inventory System for Items with Price-Dependent Demand and Time Dependent Three-Parameter Weibull Deterioration Function

In this research work we propose a mathematical model of an inventory system with time dependent three-parameter Weibull deterioration and price-dependent demand rate. The model incorporates shortages and deteriorating items are considered in which inventory is depleted not only by demand but also by decay, such as, direct spoilage as in fruits, vegetables and food products, or deterioration as in obsolete electronic components. Furthermore, the rate of deterioration is taken to be time-proportional, and a power law form of the price dependence of demand is considered. This price-dependence of the demand function is nonlinear, and is such that when price of a commodity increases, demand decreases and when price of a commodity decreases, demand increases. The objective of the model is to minimize the total inventory costs. From the numerical example presented to illustrate the solution procedure of the model, we obtain meaningful results. We then proceed to perform sensitivity analysis of our model. The sensitivity analysis illustrates the extent to which the optimal solution of the model is affected by slight changes or errors in its input parameter values.

Analysis of China's Electricity Price and Electricity Burden of Basic Industries under the Carbon Peak Target before 2030

The Chinese government is deepening reformation of electricity prices during the 14th Five Year Plan period and has set a carbon emission reduction target of reaching carbon peak before 2030.In this context,will the carbon emission target influence electricity pricing and will electricity price influence competitiveness of Chinese main industries are two questions needing to be answered.This paper compares China's electricity price level with the selected major countries in the world,and four typical industries are selected to evaluate their electricity burden respectively.Then,the correlation between residential electricity price and industrial electricity price and the influencing factors is analyzed,from the perspectives of scale,structure and technology.According to the model obtained by regression analysis,the electricity price level and corresponding residential and industrial electricity burden in 2025 and 2030 are forecasted.Index Terms-Electricity burden,industrial electricity price,regression analysis,residential electricity price.

基于Morgenstern-Price法和强度折减法的边坡稳定性对比分析

应用Morgenstern-Price法和强度折减法,对云南省宣威市万家口子水电站附近的九子崩塌堆积体的主滑体进行稳定性分析,考虑了正常蓄水位、库水位下降和地震作用时的正常蓄水位、库水位下降4种工程情况。结果表明:Morgenstern-Price法所得稳定系数随着蓄水位的下降而减小,在考虑地震作用的情况下,稳定系数也相应降低;强度折减法所得数据则不一样,蓄水位不同时,稳定系数则变化不大。相同蓄水位情况下,考虑地震时稳定系数有所减小,强度折减理论对于简单的均质土坡可能会得到与极限平衡法接近的稳定系数和滑动面,但对于非均质(如成层)土坡,则会有较大的差异性。Morgenstern-Price法使用条分法对滑动岩土体进行受力分析,针对的是人为假定的土体极限平衡条件,不能提供边坡的位移信息,需在计算中事先假定出若干个滑动面;强度折减法通过降低材料的强度参数来达到极限平衡状态,能够考虑土体的应力-应变关系,同时能给出边坡的位移信息,但是缺乏统一的边坡达到极限破坏的判断标准。2种方法都可为边坡稳定性提供有效的分析与评价。