Forecasting Methods to Reduce Inventory Level in Supply Chain

Based on the two-level supply chain composed of suppliers and retailers, we assume that market demand is subject to an ARIMA(1, 1, 1). The supplier uses the minimum mean square error method (MMSE), the simple moving average method (SMA) and the weighted moving average method (WMA) respectively to forecast the market demand. According to the statistical properties of stationary time series, we calculate the mean square error between supplier forecast demand and market demand. Through the simulation, we compare the forecasting effects of the three methods and analyse the influence of the lead-time L and the moving average parameter N on prediction. The results show that the forecasting effect of the MMSE method is the best, of the WMA method is the second, and of the SMA method is the last. The results also show that reducing the lead-time and increasing the moving average parameter improve the prediction accuracy and reduce the supplier inventory level.

Time Series Neural Network Forecasting Methods

In this paper,the possibility and key problem to construct the neural network time series model and three time series neural network forecasting methods,that is, the nerual network nonlinear time series model,neural network multi-dimension time series models and the neural network combining predictive model,are proposed.These three methods are applied to real problems.The results show that these methods are better than the traditional one.Furthermore,the neural network compared to the traditional method,and the constructed model of intellectual information forecasting system is given.

Microseismic monitoring and forecasting of dynamic disasters in underground hydropower projects in southwest China:A review

The underground hydropower projects in Southwest China is characterized by large excavation sizes,high geostresses,complicated geological conditions and multiple construction processes.Various disasters such as collapses,large deformations,rockbursts are frequently encountered,resulting in serious casualties and huge economic losses.This review mainly presents some representative results on microseismic(MS)monitoring and forecasting for disasters in hydropower underground engineering.First,a set of new denoising,spectral analysis,and location methods were developed for better identification and location of MS signals.Then,the tempo-spatial characteristics of MS events were analyzed to understand the relationship between field construction and damages of surrounding rocks.Combined with field construction,geological data,numerical simulation and parametric analysis of MS sources,the focal mechanism of MS events was revealed.A damage constitutive model considering MS fracturing size was put forward and feedback analysis considering the MS damage of underground surrounding rocks was conducted.Next,an MS multi-parameter based risk assessment and early warning method for dynamic disasters were proposed.The technology for control of the damage and deformation of underground surrounding rocks was proposed for underground caverns.Finally,two typical underground powerhouses were selected as case studies.These achievements can provide significant references for prevention and control of dynamic disasters for underground engineering with similar complicated geological conditions.

Spare Parts Demand Forecasting:a Review

Spare parts are very common in industry and military fields, and the investigations of spare parts demand forecasting methods have draws much attention in recent years. However,to the best of our knowledge,only few papers reviewed the forecasting papers systematically. This paper is an attempt to provide a novel and comprehensive view to summarize these methods. A new framework was proposed to classify the demand forecasting methods into four categories,including empirical methods,methods based on historical data,analytical methods and simulation methods. Some typical literatures related to each category were reviewed.Moreover, a general spare parts forecasting procedure was summarized and some evaluation criteria were presented. Finally,characteristics of different forecasting methods and some avenues for further research were illustrated. This work provides the managers with a systematical idea about the spare parts demand forecasting and it can be used in practical applications.

Analysis and Forecasting Model of Durative Hightemperature in Late Spring and Early Summer in 2009

This paper analyzed characteristics of Pingliang City's continuous hot weather from late spring to early summer in 2009.The result showed that,when the daily maximum temperature in some parts of cities had run up to 32℃ or above,the number of days reached the top in recent 40 years.The average temperature,average maximum temperature,surface maximum temperature and surface average temperature in most parts of the city broke history record.Based on the analysis of characteristics of the 500 hPa circulation,which resulted in durative high temperature weather,3 kinds of the high temperature circulation patterns were summarized.It was the continental warm high pressure that resulted in the durative high temperature weather in June,2009.Meanwhile,by using European numerical forecast product and MOS,the forecasting method of high temperature,from June to August,was set up.The method had been used in June,and its high-temperature forecasting accuracy in 24,48,72,96,120 hours had respectively amounted to 76.6%,69.5%,61.4%,58.1% and 51.9%.

A New Multidimensional Time Series Forecasting Method Based on the EOF Iteration Scheme

In this paper a new .mnultidimensional time series forecasting scheme based on the empirical orthogonal function (EOF) stepwise iteration process is introduced. The scheme is tested in a series of forecast experiments of Nino3 SST anomalies and Tahiti-Darwin SO index. The results show that the scheme is feasible and ENSO predictable.

OPERATIONAL ENSEMBLE FORECASTING AND ANALYSIS OF TROPICAL CYCLONES OVER THE WESTERN NORTH PACIFIC(INCLUDING THE SOUTH CHINA SEA)

Based on the tropical cyclone data from the Central Meteorological Observatory of China, Japan Meteorological Agency, Joint Typhoon Warning Center and European Centre for Medium-Range Weather Forecasts （ECMWF） during the period of 2004 to 2009, three consensus methods are used in tropical cyclone （TC） track forecasts. Operational consensus results show that the objective forecasts of ECMWF help to improve consensus skill by 2%, 3%-5% and 3%-5%, decrease track bias by 2.5 kin, 6-9 km and 10-12 km for the 24 h, 48 h and 72 h forecasts respectively over the years of 2007 to 2009. Analysis also indicates that consensus forecasts hold positive skills relative to each member. The multivariate regression composite is a method that shows relatively low skill, while the methods of arithmetic averaging and composite （in which the weighting coefficient is the reciprocal square of mean error of members） have almost comparable skills among members. Consensus forecast for a lead time of 96 h has negative skill relative to the ECMWF objective forecast.

A Forecasting Model of Vector Similarity in Phase Space for Flood and Drought over the Huanghe-Huaihe-Haihe Plain in China

To represent well the characteristics of temporal and spatial distributions, chart of 3-dekad moving total precipitation is proposed in this paper first. Then this kind of chart is expanded in terms of Chebyshev polynomial at irregular grids, and the quantitative representation of precipitation is got. Finally the Chebyshev coefficients are forecasted by using the forecasting method of vector similarity in phase space proposed by Zhou (1992). Using above mentioned procedures temporal and spatial distributions of precipitation over the Huanghe-- Huaihe-- H aihe Plain in China are forecasted.

Forecasting available parking space with largest Lyapunov exponents method

The techniques to forecast available parking space(APS) are indispensable components for parking guidance systems(PGS). According to the data collected in Newcastle upon Tyne, England, the changing characteristics of APS were studied. Thereafter, aiming to build up a multi-step APS forecasting model that provides richer information than a conventional one-step model, the largest Lyapunov exponents(largest LEs) method was introduced into PGS. By experimental tests conducted using the same dataset, its prediction performance was compared with traditional wavelet neural network(WNN) method in both one-step and multi-step processes. Based on the results, a new multi-step forecasting model called WNN-LE method was proposed, where WNN, which enjoys a more accurate performance along with a better learning ability in short-term forecasting, was applied in the early forecast steps while the Lyapunov exponent prediction method in the latter steps precisely reflect the chaotic feature in latter forecast period. The MSE of APS forecasting for one hour time period can be reduced from 83.1 to 27.1(in a parking building with 492 berths) by using largest LEs method instead of WNN and further reduced to 19.0 by conducted the new method.

Tide forecasting method based on dynamic weight distribution for operational evaluation

Through analysis of operational evaluation factors for tide forecasting, the relationship between the evaluation factors and the weights of forecasters was examined. A tide forecasting method based on dynamic weight distribution for operational evaluation was developed, and multiple-forecaster synchronous forecasting was realized while avoiding the instability cased by only one forecaster. Weights were distributed to the forecasters according to each one＇s forecast precision. An evaluation criterion for the professional level of the forecasters was also built. The eligibility rates of forecast results demonstrate the skill of the forecasters and the stability of their forecasts. With the developed tide forecasting method, the precision and reasonableness of tide forecasting are improved. The application of the present method to tide forecasting at the Huangpu Park tidal station demonstrates the validity of the method.

Wavelet time series MPARIMA modeling for power system short term load forecasting

The wavelet power system short term load forecasting(STLF) uses a mulriple periodical autoregressive integrated moving average(MPARIMA) model to model the mulriple near periodicity, nonstationarity and nonlinearity existed in power system short term quarter hour load time series, and can therefore accurately forecast the quarter hour loads of weekdays and weekends, and provide more accurate results than the conventional techniques, such as artificial neural networks and autoregressive moving average(ARMA) models test results. Obtained with a power system networks in a city in Northeastern part of China confirm the validity of the approach proposed.

Research on relations between failure heights of overburden strata and ~] / mining face parameters and forecasting method

The commercial FEM software ANSYS was used to analyze the failure characteristics of overburden strata under the conditions of different lengths of mining faces. It was shown that the parameters of mining faces, especially the length was the important factor to the failure heights and shapes of overburden strata. Fuzzy mathematics and statistical methods were used to analyze the forecasting method of the failure height of overburden strata influenced by the parameters of mining face based on the measured data under the conditions of fully-mechanized mining of general hardness cover rocks. On the basis of these analyses, a new forecasting formula was gotten. The forecasting result conforms to the in situ measured value. The result has a very important application value in safe and high-efficient mining, and has a very important advancing function to theoretical studies.

Frontiers of Value-at-Risk Forecasting for the Kuala Lumpur Composite Index of Malaysia and Bangkok SET Index of Thailand

This study utilized two forecasting methods including ARFIMA （p, d, q）-GARCH （p, d, q）, and extreme value techniques. One of the puzzling questions raised by evolutionary econometric theory is how two-way behavior is evaluated in two ways, which benefits the investors of the securities, traded on a stock exchange. For the purpose of this study, intra-day secondary data during period of 1997-2010 of the stock-market returns of Bangkok SET （Stock Exchange of Thailand） Index （Thailand） and Kuala Lumpur Composite Index （Malaysia） were collected. For the new perspective framework, the expected values were conducted using ARFIMA （p, d, q）-GARCH （p, q） forecasting method and Generalize Extreme Value （GEV） to confirm the final solutions. The Value-at-Risk （VaR） of those stock-market returns was tested. The new perspective framework of expected value confirmed that ARFIMA （1, 0.29, 1）-GARCH （1, 1） was the best forecasting method for VaR in case of the Kuala Lumpur Composite stock-market returns based on MAPE （%）. And the perspective based on extreme case confirmed that Generalize Extreme Value （GEV） as F= （x,μ,σ,ξ）： F = （x, 0.00616, 0.00573, 0.36900） was the best forecasting method for VaR in case of the Bangkok SET stock-market returns based on MAPE （%）.

CHGS method for numerical forecasting of typhoon waves-Ⅰ. Spectrum of waves in growing phase

Owing to the fact that the wind speed and direction of typhoon vary rapidly with time and space in typhoon fetch; the nearer to the typhoon eye the greater the wind velocity, and the shorter the wind fetch the smaller the wind time,as a result,the more difficult for the wind wave to fully grow. Hence.in typhoon wave numerical calculation it is impossible to use the model for a fully grown wave spectrum. Lately, the author et at. presented a CHGS method for numerical forecasting of typhoon waves, where a model for the growing wave spectrum was set up (see Eq. (2) in the text). The model involves a parameter indicating the growing degree of wind wave, i. e. ,the mean wave age β. When βvalue is small, the wave energy is chiefly concentrated near the peak frequency, so that the spectral peak gets high and steep; with the increase of β the spectral shape gradually gets lower and gentler; when β=Ⅰ, the wave fully grows, the growing spectrum becomes a fully grown P-M spectrum. The model also shows a spectral “overshooting” phenomenon within the “balance zone”.

A New Economy Forecasting Method Based on Data Barycentre Forecasting Method

A new and useful method of technology economics, parameter estimation method, was presented in light of the stability of gravity center of object in this paper. This method could deal with the fitting and forecasting of economy volume and could greatly decrease the errors of the fitting and forecasting results. Moreover, the strict hypothetical conditions in least squares method were not necessary in the method presented in this paper, which overcame the shortcomings of least squares method and expanded the application of data barycentre method. Application to the steel consumption volume forecasting was presented in this paper. It was shown that the result of fitting and forecasting was satisfactory. From the comparison between data barycentre forecasting method and least squares method, we could conclude that the fitting and forecasting results using data barycentre method were more stable than those of using least squares regression forecasting method, and the computation of data barycentre forecasting method was simpler than that of least squares method. As a result, the data barycentre method was convenient to use in technical economy.

Short-term Prediction of Occurrence Period of Main Diseases and Pests of Flos lonicerae using Phenoecology

The occurrence periods of Semiaphis heraclei Takahashi,Frankliniella sp.,Haptonchus luteolus and Microsphara linicerae Enchson wint.in Rabenh.causing damage on Flos lonicerae were investigated in F.lonicerae planting area in XinCheng county of Guangxi Province during 2008-2010,which were coincided with the occurrence periods of related phenology of local Prunus persica Rootstock.With P.persica Rootstock as indicator plant,the occurrence periods of three species of pests and one species of disease were predicted,respectively,and the method was simple and accurate,which could be the foundation for preventing these pests and diseases in the local field.

A SIMILARITY SCHEME FOR QUANTITATIVE FORECAST OF PRECIPITATION OF TYPHOONS

A quantitative scheme is put forward in our work of forecasting the storm rainfall of typhoons for specific sites.Using the initial parameters,weather situations and physical quantities as well as numerical weather prediction products,the scheme constructs multivariate,objective and similarity criteria for environmental factors for the time between the current and forthcoming moment within the domain of forecast.Through defining a non-linear similarity index,this work presents a comprehensive assessment of the similarity between historical samples of typhoons and those being forecast in terms of continuous dynamic states under the multivariate criteria in order to identify similar samples.The historical rainfall records of the similar samples are used to run weighted summarization of the similarity index to determine site-specific and quantitative forecasts of future typhoon rainfall.Samples resembling the typhoon being forecast are selected by defining a non-linear similarity index composed of multiple criteria.Trial tests have demonstrated that this scheme has positive prediction skill.

Relationship between rock uniaxial compressive strength and digital core drilling parameters and its forecast method

The rock uniaxial compressive strength(UCS)is the basic parameter for support designs in underground engineering.In particular,the rock UCS should be obtained rapidly for underground engineering with complex geological conditions,such as soft rock,fracture areas,and high stress,to adjust the excavation and support plan and ensure construction safety.To solve the problem of obtaining real-time rock UCS at engineering sites,a rock UCS forecast idea is proposed using digital core drilling.The digital core drilling tests and uniaxial compression tests are performed based on the developed rock mass digital drilling system.The results indicate that the drilling parameters are highly responsive to the rock UCS.Based on the cutting and fracture characteristics of the rock digital core drilling,the mechanical analysis of rock cutting provides the digital core drilling strength,and a quantitative relationship model(CDP-UCS model)for the digital core drilling parameters and rock UCS is established.Thus,the digital core drilling-based rock UCS forecast method is proposed to provide a theoretical basis for continuous and quick testing of the surrounding rock UCS.

Characteristics Analysis and the Early-warning Service System of Heavy Fog in Chizhou City

By analyzing the heavy fog data in Chizhou City in recent 50 years(1959-2007),the general rules of meteorological elements variations were found when the heavy fog happened.The meteorological elements included the temperature,humidity,wind direction,wind speed,air pressure and so on.The conceptual models of high-altitude and ground situation were established when the heavy fog happened in Chizhou City.Based on considering sufficiently the special geographical environment in Chizhou City,we found the key factors which affected the local heavy fog via the relative analyses.By using the statistical forecast methods which included the second-level judgment method and regression method of event probability and so on,the forecast mode equation of heavy fog was established.Moreover,the objective forecast system of heavy fog in Chizhou City was also manufactured.It provided the basis and platform which could be referred for the heavy fog forecast,service and the release of early-warning signal.

Occurrence Rule and Forecast Method of Cotton Aphid in Poyang Lake Area in Jiujiang City

[Objective] The aim was to explore occurrence rules and forecast methods of cotton aphid in Poyang Lake Area in Jiujiang City to enrich prediction methods of cotton aphid in the area. [Method] The occurrence rules and influencing factors of cotton aphid in Pengze County in 28 years were analyzed with comparative analysis, correlation analysis and wavelet analysis. Furthermore, a long-term forecast model of occurrence grade of cotton aphid and a short-term forecast model of weather condi- tion suitability were established based on stepwise regression. In addition, a forecast test was conducted in cotton area in the north of Poyang Lake. [Result] The wavelet analysis showed that in recent 28 years, oscillating period at 4 years was significant for the occurrence grade of cotton aphid in the north of Poyang Lake, but insignifi- cant for cotton at seedling stage. The comparative and correlation analyses suggest- ed that occurrence of cotton aphid is of significant correlation with winter climate and weather conditions at middle and short periods. The prediction test indicated that long-term forecast model of occurrence grade of cotton aphid and short-term forecast model of weather condition suitability based on stepwise regression can be made use of in the areas with similar climate. [Conclusion] The research provides theoreti- cal references for prevention against cotton aphid in cotton-planting area.