Design and Implementation of the Life Meteorological Index Forecasting System of Wuhu City

Based on the domestic and foreign related research methods, the life meteorological index forecasting system of Wuhu City was compiled using database and network as well as computer language. The system realized the automation process for the generation of life index forecasting products from local situation of Wuhu City and forecasting data, which could get the latest service products dispensing with manual intervention. The development of the system not only made the operation process of the life meteorological index of Wuhu City more time-saving and efficient, but also made the results more scientific and rigorous.

Using Feed Forward BPNN for Forecasting All Share Price Index

Use of artificial neural networks has become a significant and an emerging research method due to its capability of capturing nonlinear behavior instead of conventional time series methods. Among them, feed forward back propagation neural network (BPNN) is the widely used network topology for forecasting stock prices indices. In this study, we attempted to find the best network topology for one step ahead forecasting of All Share Price Index (ASPI), Colombo Stock Exchange (CSE) by employing feed forward BPNN. The daily data including ASPI, All Share Total Return Index (ASTRI), Market Price Earnings Ratio (PER), and Market Price to Book Value (PBV) were collected from CSE over the period from January 2nd 2012 to March 20th 2014. The experiment is implemented by prioritizing the number of inputs, learning rate, number of hidden layer neurons, and the number of training sessions. Eight models were selected on basis of input data and the number of training sessions. Then the best model was used for forecasting next trading day ASPI value. Empirical result reveals that the proposed model can be used as an approximation method to obtain next day value. In addition, it showed that the number of inputs, number of hidden layer neurons and the training times are significant factors that can be affected to the accuracy of forecast value.

A new hybrid method with data‑characteristic‑driven analysis for artificial intelligence and robotics index return forecasting

Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a more reliable reference in terms of artificial intelligence index investment,this paper selects the NASDAQ CTA Artificial Intelligence and Robotics(AIRO)Index as the research target,and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics.Specifically,this paper uses the ensemble empirical mode decomposition(EEMD)method and the modified iterative cumulative sum of squares(ICSS)algorithm to decompose the index returns and identify the structural breakpoints.Furthermore,it combines the least-square support vector machine approach with the particle swarm optimization method(PSO-LSSVM)and the generalized autoregressive conditional heteroskedasticity(GARCH)type models to construct innovative hybrid forecasting methods.On the one hand,the empirical results indicate that the AIRO index returns have complex structural characteristics,and present time-varying and nonlinear characteristics with high complexity and mutability;on the other hand,the newly proposed hybrid forecasting method(i.e.,the EEMD-PSO-LSSVM-ICSS-GARCH models)which considers these complex structural characteristics,can yield the optimal forecasting performance for the AIRO index returns.

Forecasting of Thunder/Lightning Potential

In this paper, the characteristics of thunderstorms in Zibo City were ana- lyzed. The thunderstorms could be divided into cold-front thunderstorm, upper trough （shear line） type thunderstorm and cold-vortex thunderstorm. We also screened out the atmosphere parameters that were highly correlated to the occurrence of thunder/ lightning. They were treated as the predictors. Each possible influencing factor was analyzed on the lightning day correspondingly. Their threshold values were also de- termined. Among them, the parameter that had the highest neutralizing capacity was treated as negative index. For different type of thunderstorm weather, the negative index was different. The application of negative indexes was also discussed. We hope to provide a valuable basis for the accurate forecasting of thunder/lightning.

Tourism Traffic Demand Prediction Using Google Trends Based on EEMD-DBN

Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand prediction. This model firstly applied dislocation weighted synthesis method to combine Google trends into a search composite index, and then it denoised the series with EEMD. EEMD extracted the high frequency noise from the original series. The low frequency series of search composite index would be used to forecast the low frequency tourism traffic series. Taking the inbound tourism in Shanghai as an example, this paper trained the model and predicted the next 12 months tourism arrivals. The conclusion demonstrated that the forecast error of EEMD-DBN model is lower remarkably than the baselines of ARIMA, GM(1,1), FTS, SVM, CES and DBN model. This revealed that nosing processing is necessary and EEMD-DBN forecast model can improve the prediction accuracy.

Calibration and Quantitative Forecast of Extreme Daily Precipitation Using the Extreme Forecast Index (EFI)

Based on the extreme forecast index (EFI) of ECMWF, the “observed” EFI (OEFI) of observation is defined and the EFI is calibrated. Then the EFI equivalent percentile (EFIEP) and EFI equivalent quantile (EFIEQ) are designed to forecast the daily extreme precipitation quantitatively. The formulation indicates that the EFIEP is correlated not only to the EFI but also to the proportion of no precipitation. This characteristic is prominent as two areas with nearly same EFIs but different proportions of no precipitation. Cases study shows that the EFIEP can forecast reliable percentile of daily precipitation and 100% percentiles are forecasted for over max extreme events. The EFIEQ is a considerable tool for quantitative precipitation forecast (QPF). Compared to the probabilistic forecast of ensemble prediction system (EPS), it is quantitative and synthesizes the advantage of extreme precipitation location forecast of EPS. Using the observations of 2311 stations of China in 2016 to verify the EFIEP and EFIEQ, the results show that the forecast biases are around 1. The threat scores (TS) for 20 years return period events are about 0.21 and 0.07 for 36 and 180 hours lead times respectively. The equivalent threat scores (ETS) are all larger than 0 and nearly equal to the TS. The TS for heavy rainfall are 0.23 and 0.07 for 36 and 180 lead times respectively. The scores are better than those of high resolution deterministic model (HRDet) and show significant forecast skills for quantitative forecast of extreme daily precipitation.

A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting

Time series forecasting plays a significant role in numerous applications,including but not limited to,industrial planning,water consumption,medical domains,exchange rates and consumer price index.The main problem is insufficient forecasting accuracy.The present study proposes a hybrid forecastingmethods to address this need.The proposed method includes three models.The first model is based on the autoregressive integrated moving average(ARIMA)statistical model;the second model is a back propagation neural network(BPNN)with adaptive slope and momentum parameters;and the thirdmodel is a hybridization between ARIMA and BPNN(ARIMA/BPNN)and artificial neural networks and ARIMA(ARIMA/ANN)to gain the benefits of linear and nonlinearmodeling.The forecasting models proposed in this study are used to predict the indices of the consumer price index(CPI),and predict the expected number of cancer patients in the Ibb Province in Yemen.Statistical standard measures used to evaluate the proposed method include(i)mean square error,(ii)mean absolute error,(iii)root mean square error,and(iv)mean absolute percentage error.Based on the computational results,the improvement rate of forecasting the CPI dataset was 5%,71%,and 4%for ARIMA/BPNN model,ARIMA/ANN model,and BPNN model respectively;while the result for cancer patients’dataset was 7%,200%,and 19%for ARIMA/BPNNmodel,ARIMA/ANN model,and BPNNmodel respectively.Therefore,it is obvious that the proposed method reduced the randomness degree,and the alterations affected the time series with data non-linearity.The ARIMA/ANN model outperformed each of its components when it was applied separately in terms of increasing the accuracy of forecasting and decreasing the overall errors of forecasting.

Forecasting Inflation Rate of Zambia Using Holt’s Exponential Smoothing

In this paper, the Holt’s exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast inflation rate of Zambia using the monthly consumer price index (CPI) data from May 2010 to May 2014. Results show that the ARIMA ((12), 1, 0) is an adequate model which best fits the CPI time series data and is therefore suitable for forecasting CPI and subsequently the inflation rate. However, the choice of the Holt’s exponential smoothing is as good as an ARIMA model considering the smaller deviations in the mean absolute percentage error and mean square error. Moreover, the Holt’s exponential smoothing model is less complicated since you do not require specialised software to implement it as is the case for ARIMA models. The forecasted inflation rate for April and May, 2015 is 7.0 and 6.6 respectively.

Establishment and Test of a Lightning Potential Prediction Index

The mesoscale forecasting model WRF is used to simulate two strong convective weather processes accompanied by lightning in Qinghai Province,and the simulated lightning potential index (LPI) and the observed lightning are compared.Finally,the application of LPI in another lightening case is examined.The results show that the simulated LPI corresponds well to the observed lightning in time.In terms of spatial distribution,the simulated LPI tends to be consistent with the observed lightning density,but there are some minor differences in the locations.Therefore,the LPI can be used to forecast lightning density.

Cultivar,Nitrogen and Irrigation Influence on Grain Quality and Its Forecasting Methods by In situ Reflected Spectrum of Winter Wheat

Field experiments were conducted to examine the influence factors of cultivar, nitrogen application and irrigation on grain protein content, gluten content and grain hardness in three winter wheat cultivars under four levels of nitrogen and irrigation treatments. Firstly, the influence of cultivars and environment factors on grain quality were studied, the effective factors were cultivars, irrigation, fertilization, etc. Secondly, total nitrogen content around winter wheat anthesis stage was proved to be significantly correlative with grain protein content, and spectral vegetation index significantly correlated to total nitrogen content around anthesis stage were the potential indicators for grain protein content. Accumulation of total nitrogen content and its transfer to grain is the physical link to produce the final grain protein, and total nitrogen content at anthesis stage was proved to be an indicator of final grain protein content. The selected normalized photochemical reflectance index (NPRI) was proved to be able to predict grain protein content on the close correlation between the ratio of total carotenoid to chlorophyll a and total nitrogen content. The method contributes towards developing optimal procedures for predicting wheat grain quality through analysis of their canopy reflected spectrum at anthesis stage. Regression equations were established to forecast grain protein and dry gluten content by total nitrogen content at anthesis stage, so it is feasible for forecasting grain quality by establishing correlation equations between biochemical constitutes and canopy reflected spectrum.

Seasonal Least Squares Support Vector Machine with Fruit Fly Optimization Algorithm in Electricity Consumption Forecasting

Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting.

Characterizing, Monitoring and Forecasting of Drought in Jordan River Basin

Jordan is very vulnerable to drought because of its location in the arid to semi-arid part of the Middle East. Droughts coupled with water scarcity are becoming a serious threat to the economic growth, social cohesion and political stability. Rainfall time series from four rain stations covering the Jordan River Basin were analyzed for drought characterization and forecasting using standardized precipitation index (SPI), Markov chain and autoregressive integrated moving average (ARIMA) model. The 7-year moving average of Amman data showed a decreasing trend while data from the other three stations were stable or showed an increasing trend. The frequency analysis indicated 2-year return period for near zero SPI values while the return period for moderate drought was 7 years. Successive droughts had occurred at least three times during the past 40 years. Severe droughts are expected once every 20 - 25 year period at all rain stations. The extreme droughts were rare events with return periods between 80 and 115 years. There are equal occurrence probabilities for drought and wet conditions in any given year, irrespective, of the condition in the previous year. The results showed that ARIMA model was successful in predicting the overall statistics with a given period at annual scales. The overall number of predicted/observed droughts during the validation periods were 2/2 severe droughts for Amman station and, 0/1, 1/1, 0/1 extreme droughts for Amman, Irbid and Mafraq stations, respectively. In addition, the ARIMA model also predicted 3 out of 4 actual moderate droughts for Amman and Mafraq stations. It was concluded that early warning of developing droughts can be deduced form the monthly Markov transitional probabilities. ARIMA models can be used as a forecasting tool of the future drought trends. Using the first and second order Markov probabilities can complement the ARIMA predictions.

Modeling Consumer Price Index in Zambia: A Comparative Study between Multicointegration and Arima Approach

Consumer Price Index (CPI) is an important indicator used to determine inflation. The main objective of this research was to compare the forecasting ability of two time-series models using Zambia Monthly Consumer Price Index. We used monthly CPI data which were collected from January 2003 to December 2017. The models that were compared are the Autoregressive Integrated Moving average (ARIMA) model and Multicointegration (ECM) model. Results show that the ECM was the best fit model of CPI in Zambia since it showed smallest errors measures. Lastly, a forecast was done using the ECM and results show an average growth rate for food CPI at 6.63% and an average growth rate for nonfood CPI at 7.41%. Forecasting CPI is an important factor for any economy because it is essential in economic planning for the future. Hence, identifying a more accurate forecasting model is a major contribution to the development of Zambia.

Forecasts of South China Sea surface temperature anomalies using the Nio indices and dipole mode index as predictors

Based on an empirical orthogonal function （EOF） analysis of the monthly NCEP Optimum Interpolation Sea Surface Temperature （OISST） data in the South China Sea （SCS） after removing the climatological mean and trends of SST, over the period of January 1982 to October 2003, the corresponding TCF correlates best with the Dipole Mode Index （DMI）, Nino1＋2, Nino3.4, Nino3, and Niflo4 indices with time lags of 10, 3, 6, 5, and 6 months, respectively. Thus, a statistical hindcasts in the prediction model are based on a canonical correlation analysis （CCA） model using the above indices as predictors spanning from 1993/1994 to 2003/2004 with a 1-12 month lead time after the canonical variants are calculated, using data from the training periods from January 1982 to December1992. The forecast model is successful and steady when the lead times are 1-12 months. The SCS warm event in 1998 was successfully predicted with lead times from 1-12 months irrespective of the strength or time extent. The prediction ability for SSTA is lower during weak ENSO years, in which other local factors should be also considered as local effects play a relatively important role in these years. We designed the two forecast models： one using both DMI and Nino indices and the other using only Nino indices without DMI, and compared the forecast accuracies of the two cases. The spatial distributions of forecast accuracies show different confidence areas. By turning off the DMI, the forecast accuracy is lower in the coastal areas off the Philippines in the SCS, suggesting some teleconnection may occur with the Indian Ocean in this area. The highest forecast accuracies occur when the forecast interval is five months long without using the DMI, while using both of Nino indices and DMI, the highest accuracies occur when the forecast interval time is eight months, suggesting that the Nino indices dominate the interannual variability of SST anomalies in the SCS. Meanwhile the forecast accuracy is evaluated over an independent test period of more than 11 years （1993/94 to October 2004） by comparing the model performance with a simple prediction strategy involving the persistence of sea surface temperature anomalies over a 1-12 month lead time （the persisted prediction）. Predictions based on the CCA model show a significant improvement over the persisted prediction, especially with an increased lead time （longer than 3 months）. The forecast model performs steadily and the forecast accuracy, i.e., the correlation coefficients between the observed and predicted SSTA in the SCS are about 0.5 in most middle and southern SCS areas, when the thresholds are greater than the 95% confidence level. For all 1 to 12 month lead time forecasts, the root mean square errors have a standard deviation of about 0.2. The seasonal differences in the prediction performance for the 1-12 month lead time are also examined.

Forecasting World Market Structure of Iran＇s Pistachio Exports

Iran is the main pistachio producer and exporter country in the world. Pistachio as a commercial output has a special importance in the agricultural production of Iran and contains large portion of non-oil exportation. The main aim of this study has been to analysis the world market and Iran＇s pistachio exports market using Hirschman-Herfindahl index. For this purpose, Iran＇s exports data over 1992-2009 and world exports statistics in 1992-2007 have been used. The results showed that diversification in target markets of Iran＇s pistachio exports increased in this period. The market structure of pistachio exports for Iran has been predominant firm and monopoly in first and end of this period, respectively. While in world market, Iran has been predominant firm except 1997 and 2007 years. According to findings of this study, it is forecasted that the competition degree of Iran＇s pistachio exports market will increase during the period 2011 to 2015.

Development and Utilization of Meteorological Tourism Resources in the Lu'an Area

Due to its superior geographical environment,the Lu’an area is rich in characteristic meteorological tourism resources.In order to make better use of the meteorological tourism advantages in the Lu’an area and create a characteristic tourism industry,based on observation data of meteorological observation station in the Lu’an area and literature data during 1990-2020,spass software was used for statistical analysis.The distribution rule of meteorological tourism resources in the Lu’an area was explored,and meteorological tourism resources in the Lu’an area were preliminary grasped.The results show that the human comfort index grade of tourism meteorology in the Lu’an area is good,and the suitable travel time can be as long as 227 d and above in a whole year.It is possible to develop characteristic tourism resources such as prediction of cloud sea,rime and flowering index in the Dabie Mountain area and the Pihe River basin,and it can also be used for the development of wet and comfortable composite health resort tourism climate.The local tourism department can integrate the above meteorological conditions to formulate eco-tourism routes or meteorological characteristic tourism routes,develop tourism meteorological service products in a targeted manner,and further promote the development and utilization of meteorological tourism resources in Lu’an.

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.

Modeling Freight Truck Trips in Birmingham Using Tour-Based Approach

This research study explores the use of an innovative freight tour-based approach to model truck trips as an alternative to the conventional trip-based approach. The tour-based approach is more realistic as it captures the intermediate stops of each truck and reflects the implications of those stops on vehicle miles traveled (VMT). The paper describes the truck tour-based model concept, and presents the framework of a truck tour-based travel demand forecasting approach. As a case study, Global Positioning System (GPS) truck data are used to determine origin, destination, and truck stops for trucks moving within the Birmingham, Alabama region. Such information is then utilized to model truck movements within the study region as individual truck tours. The tour-based model is ran, and the resulting performance measures are contrasted to those obtained from the conventional trip-based planning model used by the Regional Planning Commission of Greater Birmingham (RPCGB). This case study demonstrates the feasibility of using a tour-based freight demand forecasting model as an alternative to the conventional 4-step process currently used to estimate truck trips in the Birmingham region. The results and lessons learned from the Birmingham case study are expected to improve truck movement modeling practices in the region and advance the accuracy of truck travel demand forecasting models at other locations in the future.

基于BA-SVR混合模型的果蔬生鲜物流需求预测模型研究

本文通过构建BA-SVR混合模型对果蔬生鲜物流需求进行预测研究。首先通过互联网大数据搜索技术构建果蔬生鲜需求指数相关网络关键词词库,进而采用皮尔森(Pearson)相关分析和逐步回归选择预测因子。其次,结合果蔬自身特点以及物流市场变动因素,提出了果蔬生鲜物流指数(Fruit&Vegetable Logistic Index, FVLI)概念,分析了FVLI变动的影响变量,使其成为反映物流市场信息变动的重要指标。再次,利用蝙蝠算法(Bat Algorithm, BA)自动更新迭代参数的优势,将其引入到支持向量回归(Support Vector Regression, SVR)模型中,用于优化SVR模型中自由参数值,进而构建BA-SVR混合模型对北京市果蔬生鲜需求变化趋势进行模拟仿真及实证预测。最后根据构建的性能预测指标,通过确立的基准模型与其进行对比,评估BA-SVR混合模型性能的优劣,从而提出一种可以用于果蔬生鲜物流信息短期预测的改进方法。

CMA-GEPS极端温度预报指数及2022年夏季极端高温预报检验评估

极端预报指数(EFI)是利用集合预报获取极端天气信息的有效工具之一。为提升CMA全球集合预报系统(CMAGEPS)对极端天气的预报能力,针对CMA-GEPS历史预报数据少且再预报数据缺乏、难以合理统计模式气候分布的难题,研究利用小样本确定性预报数据形成EFI所需模式气候分布的方法。采用2020年6月15日—2022年7月22日CMA全球高分辨率(0.25°×0.25°)确定性业务预报数据,通过一种时间、空间样本扩展方法建立了与较低分辨率(0.5°×0.5°)的CMA-GEPS预报模式版本匹配的各预报时效(1—10 d)逐月模式气候分布。使用CMA-GEPS业务预报和ERA5再分析数据评估了CMA-GEPS 2 m气温EFI对2022年夏季(6—8月)中外4个代表性区域极端高温的预报能力。基于相对作用特征曲线的检验结果表明,CMA-GEPS EFI在1—10 d的短、中期预报时效上均具备区分极端高温的能力。以最大TS评分为准则,确定了用于发布极端高温预警信号的EFI临界阈值。EFI的预报能力随预报时效延长呈下降趋势,且在不同区域的表现存在差异:对中国长江中下游地区极端高温的预报能力在各时效上均优于华北地区;欧洲西部地区1—7 d时效上的EFI预报能力高于欧洲中部地区,而欧洲中部地区8—10 d时效上的EFI预报能力更好。上述结果与2 m气温的集合预报质量随预报时效与空间位置而变化有关。经济价值模型的评估结果表明,基于EFI预报信息的风险决策存在一定的经济价值和参考价值。个例分析结果进一步展现了CMA-GEPS EFI能够在中期预报时效上发出极端高温早期预警的能力。