A distance-based fuzzy time series model for exchange rates forecasting

Fuzzy time series model has been successfully employed in predicting stock prices and foreign exchange rates. In this paper, we propose a new fuzzy time series model termed as distance-based fuzzy time series (DBFTS) to predict the exchange rate. Unlike the existing fuzzy time series models which require exact match of the fuzzy logic relationships (FLRs), the distance-based fuzzy time series model uses the distance between two FLRs in selecting prediction rules. To predict the exchange rate, a two factors distance-based fuzzy time series model is constructed. The first factor of the model is the exchange rate itself and the second factor comprises many candidate variables affecting the fluctuation of exchange rates. Using the exchange rate data released by the Central Bank of Taiwan, we conducted several experiments on exchange rate forecasting. The experiment results showed that the distance-based fuzzy time series outperformed the random walk model and the artificial neural network model in terms of mean square error.     

Evolving Recurrent Neural Network using Cartesian Genetic Programming to Predict The Trend in Foreign Currency Exchange Rates

Forecasting the foreign exchange rate is an uphill task. Numerous methods have been used over the years to develop an efficient and reliable network for forecasting the foreign exchange rate. This study utilizes recurrent neural networks (RNNs) for forecasting the foreign currency exchange rates. Cartesian genetic programming (CGP) is used for evolving the artificial neural network (ANN) to produce the prediction model. RNNs that are evolved through CGP have shown great promise in time series forecasting. The proposed approach utilizes the trends present in the historical data for its training purpose. Thirteen different currencies along with the trade-weighted index (TWI) and special drawing rights (SDR) is used for the performance analysis of recurrent Cartesian genetic programming-based artificial neural networks (RCGPANN) in comparison with various other prediction models proposed to date. The experimental results show that RCGPANN is not only capable of obtaining an accurate but also a computationally efficient prediction model for the foreign currency exchange rates. The results demonstrated a prediction accuracy of 98.872 percent (using 6 neurons only) for a single-day prediction in advance and, on average, 92% for predicting a 1000 days’ exchange rate in advance based on ten days of data history. The results prove RCGPANN to be the ultimate choice for any time series data prediction, and its capabilities can be explored in a range of other fields.     

一种自组织混合模型在汇率波动性预测中的应用

汇率波动性的预测一直以来是研究金融市场者关注的焦点之一,本文拓展了一种基于自组织神经网络技术的,用于预测非平稳汇率波动性的自组织混合模型(SOMAR).SOMAR突破了传统模型对平稳性的假设,变全局建模为局部建模,使得全局非平稳数据变成局部平稳数据.同时,它也是一种基于神经元网络技术的非参数回归模型,结合传统回归模型的简易性和神经元网络算法的灵活性,拓展模型(ESOMAR)提高了对数据异构的适应性.在对汇率波动性的预测实验中,ESOMAR体现出优于传统回归模型和一些基于其它神经元网络模型的效果,并证明了它在预测金融数据方面所具有的价值.     

汇率时间序列的长记忆性分析及其建模

基于时变Hurst指数提出一种用来描述时间序列长记忆性的具有时变分数差分算子的ARFIMA模型(时变分整自回归滑动平均模型),并给出建模的步骤。然后对新台币汇率时间序列建立时变ARFIMA模型。将建立的时变ARFIMA模型与其它模型进行了比较研究。证明了时变ARFIMA模型与其他模型相比较的优效性。     

Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming

The autoregressive integrated moving average (ARIMA), which is a conventional statistical method, is employed in many fields to construct models for forecasting time series. Although ARIMA can be adopted to obtain a highly accurate linear forecasting model, it cannot accurately forecast nonlinear time series. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but explaining the meaning of the hidden layers of ANN is difficult and, moreover, it does not yield a mathematical equation. This study proposes a hybrid forecasting model for nonlinear time series by combining ARIMA with genetic programming (GP) to improve upon both the ANN and the ARIMA forecasting models. Finally, some real data sets are adopted to demonstrate the effectiveness of the proposed forecasting model.     

利用自回归模型的平稳时序数据快速辨识算法

由于自回归模型的参数估计可归结为求解一个线性方程组的问题,故其在平稳时序数据的辨识过程中具有广泛的应用场合。提出了一种基于自回归模型的快速辨识算法,首先,以递推的方式对平稳时序数据自相关函数矩阵的秩的下界值进行估计,然后,以该估计值作为自回归模型的起始阶数对系统进行依次的递阶辨识,最后,基于F检验对相邻阶次的拟合误差的变化趋势进行显著性检验,并以检验结果作为算法的结束条件。新算法在保证较高辨识精度的条件下,其计算效能及辨识精度的稳定性均优于现有的自回归模型辨识算法,实验结果验证了新算法的有效性和先进性。     

标准模型下网关口令认证密钥交换协议的通用框架

网关口令认证密钥交换协议允许用户和网关在服务器的协助下建立起一个共享的会话密钥.网关口令协议适用于无线通信环境,如GSM和3GPP等.已有的网关口令认证密钥交换协议大多缺乏严格的安全证明,或者是在随机预言模型下证明安全的.该文采用模块化的设计方法提出了在标准模型下构造网关口令协议的通用框架.通用框架可以实现双向认证并且能够抵抗不可检测在线字典攻击,因此具有更强的安全性.利用DDH假设、二次剩余假设和N次剩余假设对通用框架进行实例化可以得到不同的标准模型下可证明安全的网关口令协议.     

Applying the Hybrid Model of EMD,PSR, and ELM to Exchange Rates Forecasting

Financial time series forecasting has been a challenge for time series analysts and researchers because it is noisy, nonstationary and chaotic. To overcome this limitation, this study uses empirical mode decomposition (EMD) and phase space reconstruction (PSR) to assist in the task of financial time series forecasting. In addition, we propose an approach that combines these two data preprocessing methods with extreme learning machine (ELM). The approach contains four steps as follows. (1) EMD is used to decompose the dynamics of the exchange rate time series into several components of intrinsic mode function (IMF) and one residual component. (2) The IMF and residual time series phase space is reconstructed to reveal its unseen dynamics according to the optimum time delay \(\tau \) and embedding dimension m. (3) The reconstructed time series datasets are divided into two datasets: training and testing, in which the training datasets are used to build ELM models. (4) A regression forecast model is set up for each IMF as well as the residual component by using ELM. The final prediction results are obtained by compositing the prediction values. To verify the effectiveness of the proposed approach, four exchange rates are chosen as the forecasting targets. Compared with some existing state-of-the-art models, the proposed approach yields superior results. Academically, we demonstrated the validity and superiority of the proposed approach that integrates EMD, PSR, and ELM. Corporations or individuals can apply the results of this study to acquire accurate exchange rate information and reduce exchange rate expenses.     

A combined Independent Component Analysis–Neural Network model for forecasting exchange rate variation

The currency market is one of the most efficient markets, making it very difficult to predict future prices. Several studies have sought to develop more accurate models to predict the future exchange rate by analyzing econometric models, developing artificial intelligence models and combining both through the creation of hybrid models. This paper proposes a hybrid model for forecasting the variations of five exchange rates related to the US Dollar: Euro, British Pound, Japanese Yen, Swiss Franc and Canadian Dollar. The proposed model uses Independent Component Analysis (ICA) to deconstruct the series into independent components as well as neural networks (NN) to predict each component. This method differentiates this study from previous works where ICA has been used to extract the noise of time series or used to obtain explanatory variables that are then used in forecasting. The proposed model is then compared to random walk, autoregressive and conditional variance models, neural networks, recurrent neural networks and long–short term memory neural networks. The hypothesis of this study supposes that first deconstructing the exchange rate series and then predicting it separately would produce better forecasts than traditional models. By using the mean squared error and mean absolute percentage error as a measures of performance and Model Confidence Sets to statistically test the superiority of the proposed model, our results showed that this model outperformed the other models examined and significantly improved the accuracy of forecasts. These findings support this model’s use in future research and in decision-making related to investments.     

The impact of divorce precedents on the Japanese divorce rate

This paper undertakes a time series analysis of the Japanese divorce rate using annual data over the period 1964–2006. One of the key innovations of the paper is to use court decisions on divorce disputes to construct an index that seeks to measure how the probability of success in a divorce suit has changed over time. The computed index suggests that if it is the culpable party lodging the divorce suit, the probability of the suit being successful has clearly increased over time. The probability of winning a divorce suit appears to be an important factor in explaining the long-run rise in the Japanese divorce rate. The divorce rate also appears to be counter-cyclical.     

Mining association rules from time series to explain failures in a hot-dip galvanizing steel line

This paper presents an experience based on the use of association rules from multiple time series captured from industrial processes. The main goal is to seek useful knowledge for explaining failures in these processes. An overall method is developed to obtain association rules that represent the repeated relationships between pre-defined episodes in multiple time series, using a time window and a time lag. First, the process involves working in an iterative and interactive manner with several pre-processing and segmentation algorithms for each kind of time series in order to obtain significant events. In the next step, a search is made for sequences of events called episodes that are repeated among the various time series according to a pre-set consequent, a pre-established time window and a time lag. Extraction is then made of the association rules for those episodes that appear many times and have a high rate of hits. Finally, a case study is described regarding the application of this methodology to a historical database of 150 variables from an industrial process for galvanizing steel coils.     

Stochastic optimization for the calculation of the time dependency of the physiological demand during exercise and recovery

The stochastic optimization method ALOPEX IV is successfully applied to the problem of estimating the time dependency of the physiological demand in response to exercise. This is a fundamental and unsolved problem in the area of exercise physiology, where the lack of appropriate tools and techniques forces the assumption and the use of a constant demand during exercise. By the use of an appropriate partition of the physiological time series and by means of stochastic optimization, the time dependency of the physiological demand during heavy intensity exercise and its subsequent recovery is, for the first time, revealed.     

Atomic Decomposition of Financial Data

When looking at a time series, it is often instructive to consider the data as observations sampled from a noisy version of some underlying data generating process. This data generating process may be considered to be a function from a function space. We can specify very simple functions, known as atoms, which may be taken in linear combinations to represent any function within a particular function space. The atoms are described as members of a family of functions indexed by parameters. Quite commonly used for functions underlying time series data are the parameters location and frequency. This type of atom is known as a time-frequency atom. After we have specified the family of atoms that we wish to use to represent our underlying data generating process, the difficult problem of choosing the most effective, parsimonious representation from this family remains to be solved. Several techniques, such as Matching Pursuit and Basis Pursuit, have been suggested to solve this problem. In the current study, we investigate the use of several families of atoms, both individually and in combination, to decompose exchange rate data in search of structure that has been overlooked in more traditional approaches.     

标准模型下高效的三方口令认证密钥交换协议

三方口令认证密钥交换协议允许两个分别与服务器共享不同口令的用户在服务器的协助下建立共享的会话密钥,从而实现了用户间端到端的安全通信.现阶段,多数的三方口令认证密钥交换协议都是在随机预言模型下可证明安全的.但在实际应用中,利用哈希函数对随机预言函数进行实例化的时候会给随机预言模型下可证明安全的协议带来安全隐患,甚至将导致协议不安全.以基于ElGamal加密的平滑投射哈希函数为工具,在共同参考串模型下设计了一种高效的三方口令认证密钥交换协议,并且在标准模型下基于DDH假设证明了协议的安全性.与已有的同类协议相比,该协议在同等的安全假设下具有更高的计算效率和通信效率,因此更适用于大规模的端到端通信环境.     

Periodicity detection in time series databases

Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity, rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in O(n log n) time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns.     

Design of hybrid differential evolution and group method of data handling networks for modeling and prediction

This paper proposes a hybrid modeling approach based on two familiar non-linear methods of mathematical modeling; the group method of data handling (GMDH) and differential evolution (DE) population-based algorithm. The proposed method constructs a GMDH self-organizing network model of a population of promising DE solutions. The new hybrid implementation is then applied to modeling tool wear in milling operations and also applied to two representative time series prediction problems of exchange rates of three international currencies and the well-studied Box-Jenkins gas furnace process data. The results of the proposed DE–GMDH approach are compared with the results obtained by the standard GMDH algorithm and its variants. Results presented show that the proposed DE–GMDH algorithm appears to perform better than the standard GMDH algorithm and the polynomial neural network (PNN) model for the tool wear problem. For the exchange rate problem, the results of the proposed DE–GMDH algorithm are competitive with all other approaches except in one case. For the Box-Jenkins gas furnace data, the experimental results clearly demonstrates that the proposed DE–GMDH-type network outperforms the existing models both in terms of better approximation capabilities as well as generalization abilities. Consequently, this self-organizing modeling approach may be useful in modeling advanced manufacturing systems where it is necessary to model tool wear during machining operations, and in time series applications such as in prediction of time series exchange rate and industrial gas furnace problems.     

A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model

In the literature, there have been many studies using fuzzy time series for the purpose of forecasting. The most studied model is the first order fuzzy time series model. In this model, an observation of fuzzy time series is obtained by using the previous observation. In other words, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, this model can not be sufficient for some time series such as seasonal time series which is an important class in time series models. Besides, the time series encountered in real life have not only autoregressive (AR) structure but also moving average (MA) structure. The fuzzy time series models available in the literature are AR structured and are not appropriate for MA structured time series. In this paper, a hybrid approach is proposed in order to analyze seasonal fuzzy time series. The proposed hybrid approach is based on partial high order bivariate fuzzy time series forecasting model which is first introduced in this paper. The order of this model is determined by utilizing Box-Jenkins method. In order to show the efficiency of the proposed hybrid method, real time series are analyzed with this method. The results obtained from the proposed method are compared with the other methods. As a result, it is observed that more accurate results are obtained from the proposed hybrid method.     

Some properties of multivariate INAR(1) processes

INteger-valued AutoRegressive (INAR) processes are common choices for modeling non-negative discrete valued time series. In this framework and motivated by the frequent occurrence of multivariate count time series data in several different disciplines, a generalized specification of the bivariate INAR(1) (BINAR(1)) model is considered. In this new, full BINAR(1) process, dependence between the two series stems from two sources simultaneously. The main focus is on the specific parametric case that arises under the assumption of a bivariate Poisson distribution for the innovations of the process. As it is shown, such an assumption gives rise to a Hermite BINAR(1) process. The method of conditional maximum likelihood is suggested for the estimation of its unknown parameters. A short application on financial count data illustrates the model.     

A probabilistic justification for using tf×idf term weighting in information retrieval

This paper presents a new probabilistic model of information retrieval. The most important modeling assumption made is that documents and queries are defined by an ordered sequence of single terms. This assumption is not made in well-known existing models of information retrieval, but is essential in the field of statistical natural language processing. Advances already made in statistical natural language processing will be used in this paper to formulate a probabilistic justification for using tf×idf term weighting. The paper shows that the new probabilistic interpretation of tf×idf term weighting might lead to better understanding of statistical ranking mechanisms, for example by explaining how they relate to coordination level ranking. A pilot experiment on the TREC collection shows that the linguistically motivated weighting algorithm outperforms the popular BM25 weighting algorithm. Received: 17 December 1998 / Revised: 31 May 1999     

Development of a coupled wavelet transform and evolutionary Levenberg‐Marquardt neural networks for hydrological process modeling

This research aims to present a general framework by which the most appropriate wavelet parameters including mother wavelet, vanishing moment, and decomposition level can be chosen for a joint wavelet transform and machine learning model. This study is organized in 2 parts: the first part presents an evolutionary Levenberg‐Marquardt neural network (ELMNN) model as the most effective machine learning configuration, and the second part describes how the wavelet transform can be effectively embedded with the developed ELMNN model. In this research, the rainfall and runoff time series data of 2 distinct watersheds at 2 different time scales (daily and monthly) were used to build the proposed hybrid wavelet transform and ELMNN model. The conclusions of this study showed that the Daubechies wavelet more than other wavelet families is capable to extract the informative features of hydrologic series. The vanishing moment and decomposition level of this mother wavelet should be selected based on the watershed behavior and the time resolution of rainfall and runoff time series, respectively. The verification results for both watersheds at daily and monthly time scales indicated root mean square error, peak value criterion, low value criterion, and Kling‐Gupta efficiency as about 0.017, 0.021, 0.023, and 0.91, respectively.