Comparative Study of Volatility Forecasting Models： The Case of Malaysia, Indonesia, Hong Kong and Japan Stock Markets

This paper aims to investigate the effectiveness of four volatility forecasting models, i.e. Exponential Weighted Moving Average （EWMA）, Autoregressive Integrated Moving Average （ARIMA） and Generalized Auto-Regressive Conditional Heteroscedastic （GARCH）, in four stock markets Indonesia, Malaysia, Japan and Hong Kong. Using monthly closing stock index prices collected from 1 st January 1998 to 31 st December 2015 for the four selected countries, results obtained confirm that volatility in developed markets is not necessarily always lower than the volatility in emerging markets. Among all the three models, GARCH （1, l） model is found to be the best forecasting model for stock markets in Malaysia, Indonesia, and Japan, while EWMA model is found to be the best forecasting model for Hong Kong stock market. The outperformance of GARCH （1, 1） found supports again what is found in Minkah （2007）.

Fortified Financial Forecasting Models Based on Non-Linear Searching Approaches

The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i.e., secular trends, cyclical variations, seasonal effects, and stochastic variations）, they believe the best forecasting model is the one which realistically considers the underlying causal factors in a situational relationship and therefore has the best ＂track records＂ in generating data. Paper＇s models can be adjusted for variations in related a time series which processes a great deal of randomness, to improve the accuracy of the financial forecasts. Because of Na＇fve forecasting models are based on an extrapolation of past values for future. These models may be adjusted for seasonal, secular, and cyclical trends in related data. When a data series processes a great deal of randomness, smoothing techniques, such as moving averages and exponential smoothing, may improve the accuracy of the financial forecasts. But neither Na＇fve models nor smoothing techniques are capable of identifying major future changes in the direction of a situational data series. Hereby, nonlinear techniques, like direct and sequential search approaches, overcome those shortcomings can be used. The methodology which we have used is based on inferential analysis. To build the models to identify the major future changes in the direction of a situational data series, a comparative model building is applied. Hereby, the paper suggests using some of the nonlinear techniques, like direct and sequential search approaches, to reduce the technical shortcomings. The final result of the paper is to manipulate, to prepare, and to integrate heuristic non-linear searching methods to serve calculating adjusted factors to produce the best forecast data.

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.

The forecasting models of brown planthopper Nilaparvata Lugens(stol)in Zhejiang Provence

Based on the historical data over 15 years from fivecounties including Xiaoshan,Longyou,Pujiang,Wenling,and Huangyan,Zhejiang Province,a se-ries of forecasting models were established by stepwise regression.These models could be used to pre-dict the population size and the level of the main en-dangering generation of brown planthopper(BPH)on late-season rice.After eight years validation,73models were established from 469 ones as a series ofmodels used as long,medium,and short term fore-casting.

Wavelet Decomposition Impacts on Traditional Forecasting Time Series Models

This investigative study is focused on the impact of wavelet on traditional forecasting time-series models,which significantly shows the usage of wavelet algorithms.Wavelet Decomposition(WD)algorithm has been combined with various traditional forecasting time-series models,such as Least Square Support Vector Machine(LSSVM),Artificial Neural Network(ANN)and Multivariate Adaptive Regression Splines(MARS)and their effects are examined in terms of the statistical estimations.The WD has been used as a mathematical application in traditional forecast modelling to collect periodically measured parameters,which has yielded tremendous constructive outcomes.Further,it is observed that the wavelet combined models are classy compared to the various time series models in terms of performance basis.Therefore,combining wavelet forecasting models has yielded much better results.

New Thought of Meteorological Forecasting and Warning Models of Geological Disasters in Loess Plateau of North Shaanxi

The study established daily comprehensive precipitation equations and calculated respective critical daily comprehensive precipitation value of loess-collapse disasters and landslide disasters by dint of the geological disasters and corresponding precipitation data in 47 years.Considering geological disaster risk divisions,precipitation influence coefficient and daily comprehensive precipitation,hourly rolling daily-forecasting and hourly warning fine and no-gap models on the base of high temporal and spatial resolution rainfall data of automatic meteorological station were developed.Through the verifying of combination of dynamical forecasting model and warning model,the results showed that it can improve efficiency of forecast and have good response at the same time.

Threshold autoregression models for forecasting El Nino events

-In this paper, monthly mean SST data in a large area are used. After the spacial average of the data is carried out and the secular monthly means are substracted, a time series (Jan. 1951-Dec. 1985) of SST anomalies of the cold tongue water area in the eastern tropical Pacific Ocean is obtained. On the basis of the time series, an autoregression model, a self-exciting threshold autoregression model and an open loop autoregression model are developed respectively. The interannual variations are simulated by means of those models. The simulation results show that all the three models have made very good hindcasting for the nine El Nino events since 1951. In order to test the reliability of the open loop threshold model, extrapolated forecast was made for the period of Jan. 1986-Feb. 1987. It can be seen from the forecasting that the model could forecast well the beginning and strengthening stages of the recent El Nino event (1986-1987). Correlation coefficients of the estimations to observations are respectively 0. 84, 0. 88 and 0. 89. It is obvious that all the models work well and the open loop threshold one is the best. So the open loop threshold autoregression model is a useful tool for monitoring the SSTinterannual variation of the cold tongue water area in the Eastern Equatorial Pacific Ocean and for estimating the El Nino strength.

Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System

This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.

Dynamical Predictability of Leading Interannual Variability Modes of the Asian-Australian Monsoon in Climate Models

The dynamical prediction of the Asian-Australian monsoon(AAM)has been an important and long-standing issue in climate science.In this study,the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide.Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences,especially for the second mode.The first AAM mode,from various seasonal sequences,coincides with the El Niño phase transition in the eastern-central Pacific.The second mode,initialized from boreal summer and autumn,leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring.Our findings hint that ENSO,as an early signal,is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes.Still,the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features.The multimodel ensemble(MME)mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features.The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes.The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.

Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models

Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.

Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model

This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model（NDGM）, the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence（MAPEM） and mean percent of interval sequence simulating value set covered（MPSVSC）, are defined and, the procedure of the interval grey number sequence based the NDGM（IG-NDGM） is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM（1,1） model and GM（1,1） model.

The Water-Bearing Numerical Model and Its Operational Forecasting Experiments PartII: The Operational Forecasting Experiments

おhe water-bearing numerical model is undergone all round examinations during the operational forecasting experiments from 1994 to 1996. A lot of difficult problems arising from the model′s water-bearing are successfully resolved in these experiments through developing and using a series of technical measures. The operational forecasting running of the water-bearing numerical model is realized stably and reliably, and satisfactory forecasts are obtained.

Forecasting water disaster for a coal mine under the Xiaolangdi reservoir

Xin’an coal mine, Henan Province, faces the risk of water inrush because 40% of the area of the coal mine is under the surface water of the Xiaolangdi reservoir. To forecast water disaster, an effective aquifuge and a limit of water infiltration were determined by rock-phase analysis and long term observations of surface water and groundwater. By field monitoring, as well as physical and numerical simulation experiments, we obtained data reflecting different heights of a water flow fractured zone (WFFZ) under different mining conditions, derived a formula to calculate this height and built a forecasting model with the aid of GIS. On the basis of these activities, the coal mine area was classified into three sub-areas with different potential of water inrush. In the end, our research results have been applied in and verified by industrial mining experiments at three working faces and we were able to present a successful example of coal mining under a large reservoir.

A new grey forecasting model based on BP neural network and Markov chain

A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.

A STUDY OF THE INFLUENCE OF MICROPHYSICAL PROCESSES ON TYPHOON NIDA(2016) USING A NEW DOUBLE-MOMENT MICROPHYSICS SCHEME IN THE WEATHER RESEARCH AND FORECASTING MODEL

The basic structure and cloud features of Typhoon Nida(2016) are simulated using a new microphysics scheme(Liuma) within the Weather Research and Forecasting(WRF) model. Typhoon characteristics simulated with the Liuma microphysics scheme are compared with observations and those simulated with a commonly-used microphysics scheme(WSM6). Results show that using different microphysics schemes does not significantly alter the track of the typhoon but does significantly affect the intensity and the cloud structure of the typhoon. Results also show that the vertical distribution of cloud hydrometeors and the horizontal distribution of peripheral rainband are affected by the microphysics scheme. The mixing ratios of rain water and graupel correlate highly with the vertical velocity component and equivalent potential temperature at the typhoon eye-wall region. According to the simulation with WSM 6 scheme,it is likely that the very low typhoon central pressure results from the positive feedback between hydrometeors and typhoon intensity. As the ice-phase hydrometeors are mostly graupel in the Liuma microphysics scheme, further improvement in this aspect is required.

The Water-Bearing Numerical Model and Its Operational Forecasting Experiments Part I: The Water-Bearing Numerical Model

In first paper of articles, the physical and calculating schemes of the water-bearing numerical model are described. The model is developed by bearing all species of hydrometeors in a conventional numerical model in which the dynamic framework of hydrostatic equilibrium is taken. The main contributions are: the mixing ratios of all species of hydrometeors are added as the prognostic variables of model, the prognostic equations of these hydrometeors are introduced, the cloud physical framework is specially designed, some technical measures are used to resolve a series of physical, mathematical and computational problems arising from water-bearing; and so on. The various problems (in such aspects as the designs of physical and calculating schemes and the composition of computational programme) which are exposed in feasibility test, in sensibility test, and especially in operational forecasting experiments are successfully resolved using a lot of technical measures having been developed from researches and tests. Finally, the operational forecasting running of the water-bearing numerical model and its forecasting system is realized stably and reliably, and the fine forecasts are obtained. All of these mentioned above will be described in second paper.

STUDY ON GREY FORECASTING MODEL OF COPPER EXTRACTION RATE WITH BIOLEACHING OF PRIMARY SULFIDE ORE

A model GM （grey model） （1,1） for forecasting the rate of copper extraction during the bioleaching of primary sulphide ore was established on the basis of the mathematical theory and the modeling process of grey system theory. It was used for forecasting the rate of copper extraction from the primary sulfide ore during a laboratory microbial column leaching experiment. The precision of the forecasted results were examined and modified via ＂posterior variance examination＂. The results show that the forecasted values coincide with the experimental values. GM （1,1） model has high forecast accuracy; and it is suitable for simulation control and prediction analysis of the original data series of the processes that have grey characteristics, such as mining, metallurgical and mineral processing, etc. The leaching rate of such copper sulphide ore is low. The grey forecasting result indicates that the rate of copper extraction is approximately 20% even after leaching for six months.

A hybrid model for numerical wave forecasting and its implementation-Ⅰ.The wind wave model

The authors make an endeavor to explain why a new hybrid wave model is here proposed when several such models have already been in operation and the so- called third generation wave modej is proving attractive. This part of the paper is devoted to the wind wave model. Both deep and shallow water models have been developed, the former being actually a special case of the latter when water depth is great. The deep water model is exceptionally simple in form. Significant wave height is the only prognostic variable. In comparison with the usual methods to compute the energy input and dissipations empirically or by 'tuning', the proposed model has the merit that the effects of all source terms are combined into one term which is computed through empirical growth relations for significant waves, these relations being, relatively speaking, easier and more reliable to obtain than those for the source terms in the spectral energy balance equation. The discrete part of the model and the implementation of the model as a whole will be discussed in the second part of the present paper.

Application of Seasonal Auto-regressive Integrated Moving Average Model in Forecasting the Incidence of Hand-foot-mouth Disease in Wuhan,China

Outbreaks of hand-foot-mouth disease（HFMD） have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average（ARIMA） model for time series analysis was designed in this study. Eighty-four-month（from January 2009 to December 2015） retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination（R^2）, normalized Bayesian Information Criterion（BIC） and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as（1,0,1）（0,1,1）12, with the largest coefficient of determination（R^2=0.743） and lowest normalized BIC（BIC=3.645） value. The residuals of the model also showed non-significant autocorrelations（P_（Box-Ljung（Q））=0.299）. The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.

Research on the mining roadway displacement forecasting based on support vector machine theory

In view of the difficulty in supporting the surrounding rocks of roadway 3-411 ofFucun Coal Mine of Zaozhuang Mining Group, a deformation forecasting model was putforward based on particle swarm optimization.The kernel function and model parameterswere optimized using particle swarm optimization.It is shown that the forecast result isvery close to the real monitoring data.Furthermore, the PSO-SVM (Particle Swarm Optimization-Support Vector Machine) model is compared with the GM(1,1) model and L-M BPnetwork model.The results show that PSO-SVM method is better in the aspect of predictionaccuracy and the PSO-SVM roadway deformation pre-diction model is feasible for thelarge deformation prediction of coal mine roadway.