Improve Interval Optimization of FLR using Auto-speed Acceleration Algorithm

Inflation is a benchmark of a country's economic development. Inflation is very influential on various things, so forecasting inflation to know on upcoming inflation will impact positively. There are various methods used to perform forecasting, one of which is the fuzzy time series forecasting with maximum results. Fuzzy logical relationships (FLR) model is a very good in doing forecasting. However, there are some parameters that the value needs to be optimised. Interval is a parameter which is highly influence toward forecasting result. The utilizing optimization with hybrid automatic clustering and particle swarm optimization (ACPSO). Automatic clustering can do interval formation with just the right amount. While the PSO can optimise the value of each interval and it is providing maximum results. This study proposes the improvement in find the solution using auto-speed acceleration algorithm. Auto-speed acceleration algorithm can find a global solution which is hard to reach by the PSO and time of computation is faster. The results of the acquired solutions can provide the right interval so that the value of the FLR can perform forecasting with maximum results

Fuzzy time series analysis and prediction using swarm optimized hybrid model.

Time series forecasting has an extensive trajectory record in the fields of business, economics, energy, population dynamics, tourism, etc. where factor models, neural network models, Bayesian models are exceedingly applied for effective prediction. It has been exemplified in numerous forecasting surveys that finding an individual forecasting model to achieve the best performances for all potential situations is inadequate. Moreover, modern research endeavour has focused on a deeper understanding of the grounds. Rather than aim for designing a single superior model, it focused on the forecasting methods that are effective under certain situations. For instance, due to the qualitative nature of forecasting, a business can come up with diverse scenarios depending on the interpretation of data. Therefore, the organizations never rely on any individual forecasting model solely, rather focused on sets of individual models to attain the best possible knowledge of the future. The time series forecasting model has a great impact in terms of prediction. Many forecasting models related to fuzzy time series were proposed in the past decades. These models were widely applied to various problem domains, especially in dealing with forecasting problems where historical data are linguistic values. A hybrid forecasting method can be effective to improve forecast accuracy by merging sets of the individual forecasting models. Numerous hybrid forecasting models have been proposed last couple of years that combined fuzzy time series with the evolutionary algorithms, but the performance of the models is not quite satisfactory. In this research, a novel hybrid fuzzy time series forecasting model is proposed that used the historical data as the universe of discourse and the automatic clustering algorithm to cluster the universe of discourse by adjusting the clusters into intervals. Furthermore, the particle swarm optimization algorithm is also examined to improve forecasted accuracy. The proposed method is considered to forecast student enrolment of the University of Alabama. The model achieves a significant improvement in forecast accuracy as compared to state-of-the-art hybrid fuzzy time series forecasting models. It is obvious from the literature that no forecasting technique is appropriate for all situations. There is substantial evidence to demonstrate that combining individual forecasts produces gains in forecasting accuracy. The addition of quantitative forecasts to qualitative forecasts may reduce forecast accuracy. Individual forecasts are combined based on either the simple arithmetic average method or an artificial neural network. Research has not yet revealed the conditions for the optimal forecast combinations. This thesis provides a few contributions to enhance the existing combination model. A set of Individual forecasting models is used to form a novel combination forecasting model based on the characteristics of resulting forecasts. All methods derived in this thesis are thoroughly tested on several standard datasets. The related characteristics of the resulting forecasts are observed to have different error decompositions both for hybrid and combination forecasting model. Advanced combination structures are investigated to take advantage of the knowledge of the forecast generation processes

The cross-association relation based on intervals ratio in fuzzy time series

The fuzzy time series (FTS) is a forecasting model based on linguistic values. This forecasting method was developed in recent years after the existing ones were insufficiently accurate. Furthermore, this research modified the accuracy of existing methods for determining and the partitioning universe of discourse, fuzzy logic relationship (FLR), and variation historical data using intervals ratio, cross association relationship, and rubber production Indonesia data, respectively. The modifed steps start with the intervals ratio to partition the determined universe discourse. Then the triangular fuzzy sets were built, allowing fuzzification. After this, the FLR are built based on the cross association relationship, leading to defuzzification. The average forecasting error rate (AFER) was used to compare the modified results and the existing methods. Additionally, the simulations were conducted using rubber production Indonesia data from 2000-2020. With an AFER result of 4.77%<10%, the modification accuracy has a smaller error than previous methods, indicating  very good forecasting criteria. In addition, the coefficient values of D1 and D2 were automatically obtained from the intervals ratio algorithm. The future works modified the partitioning of the universe of discourse using frequency density to eliminate unused partition intervals

Triangular Fuzzy Time Series for Two Factors High-order based on Interval Variations

Fuzzy time series (FTS) firstly introduced by Song and Chissom has been developed to forecast such as enrollment data, stock index, air pollution, etc. In forecasting FTS data several authors define universe of discourse using coefficient values with any integer or real number as a substitute. This study focuses on interval variation in order to get better evaluation. Coefficient values analyzed and compared in unequal partition intervals and equal partition intervals with base and triangular fuzzy membership functions applied in two factors high-order. The study implemented in the Shen-hu stock index data. The models evaluated by average forecasting error rate (AFER) and compared with existing methods. AFER value 0.28% for Shen-hu stock index daily data. Based on the result, this research can be used as a reference to determine the better interval and degree membership value in the fuzzy time series.

A NEW HYBRID FUZZY TIME SERIES FORECASTING MODEL BASED ON COMBINING FUZZY C-MEANS CLUSTERING AND PARTICLE SWAM OPTIMIZATION

Fuzzy time series (FTS) model is one of the effective tools that can be used to identify factors in order to solve the complex process and uncertainty. Nowadays, it has been widely used in many forecasting problems. However, establishing effective fuzzy relationships groups, finding proper length of each interval, and building defuzzification rule are three issues that exist in FTS model. Therefore, in this paper, a novel FTS forecasting model based on fuzzy C-means (FCM) clustering and particle swarm optimization (PSO) was developed to enhance the forecasting accuracy. Firstly, the FCM clustering is used to divide the historical data into intervals with different lengths. After generating interval, the historical data is fuzzified into fuzzy sets. Following, fuzzy relationship groups were established based on the appearance history of the fuzzy sets on the right-hand side of the fuzzy logical relationships with the aim to serve for calculating the forecasting output.  Finally, the proposed model combined with PSO algorithm was applied to adjust interval lengths and find proper intervals in the universe of discourse for obtaining the best forecasting accuracy. To verify the effectiveness of the forecasting model, three numerical datasets (enrolments data of the University of Alabama, the Taiwan futures exchange –TAIFEX data and yearly deaths in car road accidents in Belgium) are selected to illustrate the proposed model. The experimental results indicate that the proposed model is better than any existing forecasting models in term of forecasting accuracy based on the first – order and high-order FTS

An Overview of Electricity Demand Forecasting Techniques

Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. Accurate models for electric power load forecasting are essential to the operation and planning of a utility company. Load forecasts are extremely important for energy suppliers and other participants in electric energy generation, transmission, distribution and markets. This paper presents a review of electricity demand forecasting techniques. The various types of methodologies and models are included in the literature. Load forecasting can be broadly divided into three categories: short-term forecasts which are usually from one hour to one week, medium forecasts which are usually from a week to a year, and long-term forecasts which are longer than a year.  Based on the various types of studies presented in these papers, the load forecasting techniques may be presented in three major groups: Traditional Forecasting technique, Modified Traditional Technique and Soft Computing Technique. Keywords: Electricity Demand, Forecasting Techniques, Soft Computing, Regression method, SVM

A refined approach for forecasting based on neutrosophic time series

This research introduces a neutrosophic forecasting approach based on neutrosophic time series (NTS). Historical data can be transformed into neutrosophic time series data to determine their truth, indeterminacy and falsity functions. The basis for the neutrosophication process is the score and accuracy functions of historical data. In addition, neutrosophic logical relationship groups (NLRGs) are determined and a deneutrosophication method for NTS is presented. The objective of this research is to suggest an idea of first-and high-order NTS. By comparing our approach with other approaches, we conclude that the suggested approach of forecasting gets better results compared to the other existing approaches of fuzzy, intuitionistic fuzzy, and neutrosophic time series

Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods

Nature inspired computational intelligence for financial contagion modelling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market