An Elman Model Based on GMDH Algorithm for Exchange Rate Forecasting

Since the Elman Neural Networks was proposed, it has attracted wide attention. This method has fast convergence and high prediction accuracy. In this study, a new hybrid model that combines the Elman Neural Networks and the group method of data handling (GMDH) is used to forecast the exchange rate. The GMDH algorithm is used for system modeling. Input variables are selected by the external standards. Based on the output of the GMDH algorithm, valid input variables can be used as an input for the Elman Neural Networks for time series prediction. The empirical results show that the new hybrid algorithm is a useful tool.

A study of boundedness in probabilistic normed spaces

It was shown in Lafuerza-Guillén, Rodríguez- Lallena and Sempi (1999) that uniform boundedness in a Serstnev PN space (V,\un, \tau,\tau^*), (named boundeness in the present setting) of a subset A in V with respect to the strong topology is equivalent to the fact that the probabilistic radius R_A of A is an element of D^+. Here we extend the equivalence just mentioned to a larger class of PN spaces, namely those PN spaces that are topological vector spaces(briefly TV spaces), but are not Serstnev PN spaces. We present a characterization of those PN spaces, whether they are TV spaces or not, in which the equivalence holds. Then a charaterization of the Archimedeanity of triangle functions \tau^* of type \tau_{T,L} is given.This work is a partial solution to a problema of comparing the concepts of distributional boundedness (D-bounded in short) and that of boundedness in the sense of associated strong topology

Countable products of probabilistic normed spaces

Countable products of probabilistic normed spaces are introduced and studied. In particular, a comparison is made with the analogous constructions for probabilistic metric spaces

Performance of Private Enterprises Under the Background of New Round of Expansion of State-Owned Enterprises

In this paper, we used the super-efficient DEA method to analyze the performance of Chinese private enterprises. Talked 100 best private enterprises from 2008 to 2012 in China as the representative, we analyze the performance of private enterprises. The results showed that the efficiency levels of the private enterprises had continuously improved from 2008 to 2012, but the overall efficiency level of the private enterprise was lower. There existed large different among the private companies, and nearly half of the efficiency values of the enterprises were at 0.7658 or less. Therefore, the state should pay more attention to the living environment of the private enterprises, the private enterprises should play its due the potential to promote the country’s sustainable development

Total boundedness in probabilistic normed spaces

In this paper, we study total boundedness in probabilistic normed space and we give criterion for total boundedness and D-boundedness in these spaces. Also we show that in general a totally bounded set is not D-bounded

Lévy Process-Driven Asymmetric Heteroscedastic Option Pricing Model and Empirical Analysis

This paper describes the peak, fat tail, and skewness characteristics of asset price via a Lévy process. It applies asymmetric GARCH model to depict asset price’s random volatility characteristics and builds a GARCH-Lévy option pricing model with random jump characteristics. It also uses circular maximum likelihood estimation technology to improve the stability of model parameter estimation. In order to test the model’s pricing results, we use Hong Kong Hang Seng Index (HSI) price data and its option data to carry out empirical studies. Results prove that the pricing bias of EGARCH-Lévy model is lower than that of standard Heston-Nandi (HN) model in the financial industry. For short-term, middle-term, and long-term European-style options, the pricing error of EGARCH-Lévy model is the lowest

A Duopoly Manufacturers’ Game Model Considering Green Technology Investment under a Cap-and-Trade System

This research studied the duopoly manufacturers’ decision-making considering green technology investment and under a cap-and-trade system. It was assumed there were two manufacturers producing products which were substitutable for one another. On the basis of this assumption, the optimal production capacity, price, and green technology investment of the duopoly manufacturers under a cap-and-trade system were obtained. The increase or decrease of the optimal production quantity of the duopoly manufacturers under a cap-and-trade system was decided by their green technology level. The increase of the optimal price as well as the increase or decrease of the maximum expected profits were decided by the initial carbon emission quota granted by the government. Our research indicates that the carbon emission of unit product is inversely proportional to the market share of an enterprise and becomes an important index to measure the core competitiveness of an enterprise

Multi-Step Ahead Natural Gas Consumption Forecasting Based on a Hybrid Model: Case Studies in The Netherlands and the United Kingdom

With worldwide activities of carbon neutrality, clean energy is playing an important role these days. Natural gas (NG) is one of the most efficient clean energies with less harmful emissions and abundant reservoirs. This work aims at developing a swarm intelligence-based tool for NG forecasting to make more convincing projections of future energy consumption, combining Extreme Gradient Boosting (XGBoost) and the Salp Swarm Algorithm (SSA). The XGBoost is used as the core model in a nonlinear auto-regression procedure to make multi-step ahead forecasting. A cross-validation scheme is adopted to build a nonlinear programming problem for optimizing the most sensitive hyperparameters of the XGBoost, and then the nonlinear optimization is solved by the SSA. Case studies of forecasting the Natural gas consumption (NGC) in the United Kingdom (UK) and Netherlands are presented to illustrate the performance of the proposed hybrid model in comparison with five other intelligence optimization algorithms and two other decision tree-based models (15 hybrid schemes in total) in 6 subcases with different forecasting steps and time lags. The results show that the SSA outperforms the other 5 algorithms in searching the optimal parameters of XGBoost and the hybrid model outperforms all the other 15 hybrid models in all the subcases with average MAPE 4.9828% in NGC forecasting of UK and 9.0547% in NGC forecasting of Netherlands, respectively. Detailed analysis of the performance and properties of the proposed model is also summarized in this work, which indicates it has high potential in NGC forecasting and can be expected to be used in a wider range of applications in the future

Multi-Step Ahead Natural Gas Consumption Forecasting Based on a Hybrid Model: Case Studies in The Netherlands and the United Kingdom

With worldwide activities of carbon neutrality, clean energy is playing an important role these days. Natural gas (NG) is one of the most efficient clean energies with less harmful emissions and abundant reservoirs. This work aims at developing a swarm intelligence-based tool for NG forecasting to make more convincing projections of future energy consumption, combining Extreme Gradient Boosting (XGBoost) and the Salp Swarm Algorithm (SSA). The XGBoost is used as the core model in a nonlinear auto-regression procedure to make multi-step ahead forecasting. A cross-validation scheme is adopted to build a nonlinear programming problem for optimizing the most sensitive hyperparameters of the XGBoost, and then the nonlinear optimization is solved by the SSA. Case studies of forecasting the Natural gas consumption (NGC) in the United Kingdom (UK) and Netherlands are presented to illustrate the performance of the proposed hybrid model in comparison with five other intelligence optimization algorithms and two other decision tree-based models (15 hybrid schemes in total) in 6 subcases with different forecasting steps and time lags. The results show that the SSA outperforms the other 5 algorithms in searching the optimal parameters of XGBoost and the hybrid model outperforms all the other 15 hybrid models in all the subcases with average MAPE 4.9828% in NGC forecasting of UK and 9.0547% in NGC forecasting of Netherlands, respectively. Detailed analysis of the performance and properties of the proposed model is also summarized in this work, which indicates it has high potential in NGC forecasting and can be expected to be used in a wider range of applications in the future