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.

辽西地区区域性大雾气候特征及分析预报(英文)

By using the fog data from 1995 to 2004 of four selected observation stations,the weather features of foggy days in Liaoxi area have been studied in this paper.The favorable surface and upper circulation for fog and its frequency have also been concluded from the statistic.In this paper,the forecasting index of fog,proposed on the basis of the condition and mechanism of the fog occurrence,has been tested by the 10-year analysis.Another test conducted by using the data of 1st July-31st December,2004 also gives a good result which has a vacancy rate of 22.2% and a miss rate of 5.1%.

Profit Guided or Statistical Error Guided? A Study of Stock Index Forecasting Using Support Vector Regression

Stock index forecasting has been one of the most widely investigated topics in the field of financial forecasting. Related studies typically advocate for tuning the parameters of forecasting models by minimizing learning errors measured using statistical metrics such as the mean squared error or mean absolute percentage error. The authors argue that statistical metrics used to guide parameter tuning of forecasting models may not be meaningful, given the fact that the ultimate goal of forecasting is to facilitate investment decisions with expected profits in the future. The authors therefore introduce the Sharpe ratio into the process of model building and take it as the profit metric to guide parameter tuning rather than using the commonly adopted statistical metrics. The authors consider three widely used trading strategies, which include a na¨?ve strategy, a filter strategy and a dual moving average strategy, as investment scenarios. To verify the effectiveness of the proposed profit guided approach, the authors carry out simulation experiments using three global mainstream stock market indices. The results show that profit guided forecasting models are competitive, and in many cases produce significantly better performances than statistical error guided models. This implies thatprofit guided stock index forecasting is a worthwhile alternative over traditional stock index forecasting practices.

Research on strong earthquake type division and forecast method for subsequent strong earthquakes

The relationships between energy, amplitude and frequency of eanhquake are correlative with the property of the seismic source. And the grade of the correlativity can be used as an index to distinguish the types of strong earth quakes. Primarily the strong earthquake can be divided into three types of main-after earthquakes, double-main earthquakes and swarm of strong earthquake. There are similarity and a certain repeatability at the quantificational indexes of hypocenter property between the same type of strong earthquakes, which supply basis for the forecast of subsequent strong shocks. The reference indexes of after strong shock forecast which are valuable for the applica tions of the method of type-divided forecast come from the analysis about more than fifty strong shock wide-band (BPZ wave) recording data of CDSN from 1988 to 1997.

A New Prediction Model for Grain Yield in Northeast China Based on Spring North Atlantic Oscillation and Late-Winter Bering Sea Ice Cover

Accurate estimations of grain output in the agriculturally important region of Northeast China are of great strategic significance for guaranteeing food security.New prediction models for maize and rice yields are built in this paper based on the spring North Atlantic Oscillation index and the Bering Sea ice cover index.The year-to-year increment is first forecasted and then the original yield value is obtained by adding the historical yield of the previous year.The multivariate linear prediction model of maize shows good predictive ability,with a low normalized root-mean-square error（NRMSE）of 13.9%,and the simulated yield accounts for 81%of the total variance of the observation.To improve the performance of the multivariate linear model,a combined forecasting model of rice is built by considering the weight of the predictors.The NRMSE of the model is 12.9%and the predicted rice yield explains 71%of the total variance.The corresponding cross-validation test and independent samples test further demonstrate the efficiency of the models.It is inferred that the statistical models established here by applying year-to-year increment approach could make rational prediction for the maize and rice yield in Northeast China before harvest.The present study may shed new light on yield prediction in advance by use of antecedent large-scale climate signals adequately.

Study on Ensemble-Based Forecast of Extremely Heavy Rainfalls in China: Experiments for July 2011 Cases

According to the Anderson-Darling principle, a method for forecast of extremely heavy rainfall （abbre- viated as extreme rainfall/precipitation） was developed based on the ensemble forecast data of the T213 global ensemble prediction system （EPS） of the China Meteorological Administration （CMA）. Using the T213 forecast precipitation data during 2007-2010 and the observed rainfall data in June-August of 2001 2010, characteristics of the cumulative distribution functions （CDFs） of the observed and the T213 EPS forecast precipitation were analyzed. Accordingly, in the light of the continuous differences of the CDFs between model climate and EPS forecasts, a mathematical model of Extreme Precipitation Forecast Index （EPFI） was established and applied to forecast experiments of several extreme rainfall events in China during 17-31 July 2011. The results show that the EPFI has taken advantage of the tail information of the model climatic CDF and provided agreeable forecasts of extreme rainfalls. The EPFI based on the T213 EPS is useful for issuing early warnings of extreme rainfalls 3 7 days in advance. With extension of the forecast lead time, the EPFI becomes less skillful. The results also demonstrate that the rationality of the model climate CDF was of vital importance to the skill of EPFI.

DIAGNOSTIC ANALYSIS ON THE DYNAMICS AND NUMERICAL SIMULATION OF CLEAR AIR TURBULENCE(CAT)

Clear air turbulence(CAT),a meso-or microscale(subgrid scale)phenomenon occurring in synoptic scale flow field at high altitude,is very difficult to be observed by the conventional obser- vation network.Thus it is necessary to approach an index to predict CAT.But at first,the struc- ture characteristics of CAT should be preanalyzed.In this paper,based on the theoretical and diag- nostic analysis of a case,features for wind profile,energy budget and dynamic mechanism of this case were presented.Furthermore,an objective and quantitative index for CAT forecast was giv- en.The verification for its efficiency was done with both real-time observation data and products from a numerical model.The results are very encouraging.