Hossein Javedani Sadaei

Stock market forecasting by using a hybrid model of exponential fuzzy time series

The initial aim of this study is to propose a hybrid method based on exponential fuzzy time series and learning automata based optimization for stock market forecasting. For doing so, a two-phase approach is introduced. In the first phase, the optimal lengths of intervals are obtained by applying a conventional fuzzy time series together with learning automata swarm intelligence algorithm to tune the length of intervals properly. Subsequently, the obtained optimal lengths are applied to generate a new fuzzy time series, proposed in this study, named exponential fuzzy time series. In this final phase, due to the nature of exponential fuzzy time series, another round of optimization is required to estimate certain method parameters. Finally, this model is used for future forecasts. In order to validate the proposed hybrid method, forty-six case studies from five stock index databases are employed and the findings are compared with well-known fuzzy time series models and classic methods for time series. The proposed model has outperformed its counterparts in terms of accuracy. In this study a two phase approach is proposed based on exponential fuzzy time series and learning automata.In the first phase, the optimal lengths of intervals are estimated by applying LA based EAs in training set.Second phase aim is to estimate certain adjusting parameters for minimizing errors in training set.The conventional FTS in the first phase is applied and in the second phase EFTS is employed.Forty six case studies from five stock index databases are employed in extensive experiments.

Short-term load forecasting method based on fuzzy time series, seasonality and long memory process

Seasonal Auto Regressive Fractionally Integrated Moving Average (SARFIMA) is a well-known model for forecasting of seasonal time series that follow a long memory process. However, to better boost the accuracy of forecasts inside such data for nonlinear problem, in this study, a combination of Fuzzy Time Series (FTS) with SARFIMA is proposed. To build the proposed model, certain parameters requires to be estimated. Therefore, a reliable Evolutionary Algorithm namely Particle Swarm Optimization (PSO) is employed. As a case study, a seasonal long memory time series, i.e., short term load consumption historical data, is selected. In fact, Short Term Load Forecasting (STLF) plays a key role in energy management systems (EMS) and in the decision making process of every power supply organization. In order to evaluate the proposed method, some experiments, using eight datasets of half-hourly load data from England and France for the year 2005 and four data sets of hourly load data from Malaysia for the year 2007, are designed. Although the focus of this research is STLF, six other seasonal long memory time series from several interesting case studies are employed to better evaluate the performance of the proposed method. The results are compared with some novel FTS methods and new state-of-the-art forecasting methods. The analysis of the results indicates that the proposed method presents higher accuracy than its counterparts, representing an efficient hybrid method for load forecasting problems. To increase accuracy of forecasts inside seasonal long memory time series, a hybrid method is proposed.The proposed method is based on a combination of Fuzzy Time Series and SARFIMA.High-order Fuzzy Time Series is adopted to be revised for developing the proposed method.Particle Swarm Optimization is applied for parameters estimation.Many long memory seasonal datasets, including short term load data are employed for evaluation purpose.

A cooperative coevolutionary algorithm for the Multi-Depot Vehicle Routing Problem

We introduce a cooperative coevolutionary algorithm for the Multi-Depot VRP.We propose an ES with variable length genotype coupled with local search operators.The proposed approach produces high-quality solutions in low computational time.The performance is not greatly affected by the overlap between subproblems.The proposed method could find improved solutions in many instances. The Multi-Depot Vehicle Routing Problem (MDVRP) is an important variant of the classical Vehicle Routing Problem (VRP), where the customers can be served from a number of depots. This paper introduces a cooperative coevolutionary algorithm to minimize the total route cost of the MDVRP. Coevolutionary algorithms are inspired by the simultaneous evolution process involving two or more species. In this approach, the problem is decomposed into smaller subproblems and individuals from different populations are combined to create a complete solution to the original problem. This paper presents a problem decomposition approach for the MDVRP in which each subproblem becomes a single depot VRP and evolves independently in its domain space. Customers are distributed among the depots based on their distance from the depots and their distance from their closest neighbor. A population is associated with each depot where the individuals represent partial solutions to the problem, that is, sets of routes over customers assigned to the corresponding depot. The fitness of a partial solution depends on its ability to cooperate with partial solutions from other populations to form a complete solution to the MDVRP. As the problem is decomposed and each part evolves separately, this approach is strongly suitable to parallel environments. Therefore, a parallel evolution strategy environment with a variable length genotype coupled with local search operators is proposed. A large number of experiments have been conducted to assess the performance of this approach. The results suggest that the proposed coevolutionary algorithm in a parallel environment is able to produce high-quality solutions to the MDVRP in low computational time.

Combining ARFIMA models and fuzzy time series for the forecast of long memory time series

Long memory time series are stationary processes in which there is a statistical long range dependency between the current value and values in different times of the series. Therefore, in this class of series, there is a slow decay of the autocorrelation function as the time difference increases. Many practical forecasting problems fall in this class, for instance, in financial time series, hydrology and earth sciences applications. This research introduces a hybrid method combining Auto Regressive Fractional Integrated Moving Average (ARFIMA) models and Fuzzy Time Series (FTS) for the forecast of long memory (long-range) time series. The proposed method is developed as one algorithm consisting of two phases. The first phase is related to the autoregressive part of the model, while the second phase is related to the Moving Average part. Based on these ideas, the combined ARFIMA and FTS model is introduced. For the parameter estimation of the model, Particle Swarm Optimization (PSO) method is selected, based on its performance on similar optimization problems. In order to illustrate the benefit and potential of the proposed ARFIMA-FTS method, it has been applied to the two stock index databases, namely Taiwan Capitalization Weighted Stock Index (TAIEX)and Dow Jones Industrial Average (DJIA), together with exchange rate data of nine main currencies versus USD. Based on the reported results, it is possible to conclude the superiority of the proposed hybrid method, compared with classical ARFIMA models and other methods in the literature.

Multilayer stock forecasting model using fuzzy time series

After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS.

Introducing polynomial fuzzy time series

Using polynomial concept and non-liner optimization enhanced the performance of Chens 1996 and Yus 2005b methods as the two frequently used methods in fuzzy time series model. To this end, polynomial schemes were given to each fuzzy logical relationship groups that had been established through forecast process to establish non-linear optimization systems. The optimal solutions of this system were applied in corresponding steps of algorithms to obtain new weights. To validate model reliability and its effectiveness, the forecasts of two huge databases namely 5 years Taiwans stock index and 2010 load data of Power Supply Company in Johor Bahru in Malaysia were then exposed to the proposed model. Next, the forecasts were compared with real values in testing datasets. The evaluation of measuring criteria namely RMSEs and MAPEs showed that the proposed model could produce accurate forecast compared with the Chens and Yus method in fuzzy time series. The implication of this study is to generalize the results to other fuzzy time series models.

Improving TAIEX forecasting using fuzzy time series with Box–Cox power transformation

Box–Cox together with our newly proposed transformation were implemented in three different real world empirical problems to alleviate noisy and the volatility effect of them. Consequently, a new domain was constructed. Subsequently, universe of discourse for transformed data was established and an approach for calculating effective length of the intervals was then proposed. Considering the steps above, the initial forecasts were performed using frequently used fuzzy time series (FTS) methods on transformed data. Final forecasts were retrieved from initial forecasted values by proper inverse operation. Comparisons of the results demonstrate that the proposed method produced more accurate forecasts compared with existing FTS on original data.

Interval forecasting with Fuzzy Time Series

In recent years, the demand for developing low computational cost methods to deal with uncertainty in forecasting has been increased. Interval forecasting is a category of forecasting in which the method provides intervals as outputs of its forecasting. The initial aim of this paper is therefore proposing a new interval forecasting method based on a low cost and accurate forecasting method, namely first order Fuzzy Time Series. In this study, the results of the proposed method are compared with actual data and regular point forecasts using Fuzzy Time Series. The evaluation of the results shows the accuracy and promising performance of the proposed method.

A hybrid SARFIMA-FTS model for time series prediction in smart grids

The intensive use of electricity in life and modern society implies increasing demand and the need for increasingly high reliability. Smart Grids (SG) are the next technological breakthrough to be achieved for the generation, transmission and distribution of energy. Historically, it has been sought to automate each of these systems to perform main and ancillary services. Robust forecasting methodologies are essential for planning and operation of SG. However, SG data presents the characteristics of long memory time series, which are a kind of stationary processes in such a way that there is always a statistical long range dependency between the current value and values in different times of the series. Motivated by this, we introduce a new hybrid model combining Seasonal Auto Regressive Fractionally Integrated Moving Average with high order Fuzzy Time Series (SARFIMA-FTS). We present comparative results of SARFIMA-FTS with other two methodologies solutions in microgrid data. The computational results show that the performance of the proposed SARFIMA-FTS method is quite competitive with other presented methods in literature using less parameters, hence it is an important tool for prediction in smart grids.

Very short-term solar forecasting using fuzzy time series

Solar Photovoltaics is a source of energy very sensitive to climate variations. Therefore, it is very important to apply a forecasting method to a PV system. Solar forecasting provides extremely useful information for tasks such as management of electricity grids and solar energy trading, where good accuracy and good performance are desirable goals for a very short term forecasting model. In this paper, we propose the use of fuzzy time series (FTS) techniques to this problem. Specifically, Chens first order and high-order FTS methods and the Weighted FTS method are compared with other forecasting models widely used to approach solar irradiance forecasting. We evaluate the performance of FTS methods and different forecasting techniques to solve very short-term solar forecasting problems. The results show that FTS methods are able to achieve significant improvements in forecasting accuracy and performance if compared to other forecasting methods. A discussion on how to improve the forecasting performance of FTS models is also provided.