A REVIEW OF PROBABILISTIC GRAPH MODELS FOR FEATURE SELECTION WITH APPLICATIONS IN ECONOMIC AND FINANCIAL TIME SERIES FORECASTING

In every field of life, people are interested to be able to forecast future.  A number of techniques are available to predict and forecasting upto a certain level of accuracy. Many techniques involve statistical tools and techniques for forecasting, modeling and control. Use of statistical techniques is growing with time and new techniques are being developed very rapidly. Especially in the field of economics and finance, the estimation and forecasting of economic and financial indicators play a vital role in decision making. Many models are developed in the last 2 decades to get better accuracy and efficiency in time series analysis and still there is a scope of learning and getting betterment in this field is available. In this research we have reviewed probability graphs, directed acyclic graphs, Bayesian networks, feature selection algorithms and Markov blankets for time series forecasting on the economic and financial problems (like stock exchange forecasting, multi-objective business risk analysis, consumers’ analysis, portfolio optimization, credit scoring etc). This is a new dimension for adaptive modeling techniques in economics and finance modeling

A Quantum based Evolutionary Algorithm for Stock Index and Bitcoin Price Forecasting

Quantum computing has emerged as a new dimension with various applications in different fields like robotic, cryptography, uncertainty modeling etc. On the other hand, nature inspired techniques are playing vital role in solving complex problems through evolutionary approach. While evolutionary approaches are good to solve stochastic problems in unbounded search space, predicting uncertain and ambiguous problems in real life is of immense importance. With improved forecasting accuracy many unforeseen events can be managed well. In this paper a novel algorithm for Fuzzy Time Series (FTS) prediction by using Quantum concepts is proposed in this paper. Quantum Evolutionary Algorithm (QEA) is used along with fuzzy logic for prediction of time series data. QEA is applied on interval lengths for finding out optimized lengths of intervals producing best forecasting accuracy. The algorithm is applied for forecasting Taiwan Futures Exchange (TIAFEX) index as well as for Bitcoin crypto currency time series data as a new approach. Model results were compared with many preceding algorithms

The Forecasting of Labour Force Participation and the Unemployment Rate in Poland and Turkey Using Fuzzy Time Series Methods

Fuzzy time series methods based on the fuzzy set theory proposed by Zadeh (1965) was first introduced by Song and Chissom (1993). Since fuzzy time series methods do not have the assumptions that traditional time series do and have effective forecasting performance, the interest on fuzzy time series approaches is increasing rapidly. Fuzzy time series methods have been used in almost all areas, such as environmental science, economy and finance. The concepts of labour force participation and unemployment have great importance in terms of both the economy and sociology of countries. For this reason there are many studies on their forecasting. In this study, we aim to forecast the labour force participation and unemployment rate in Poland and Turkey using different fuzzy time series methods

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

A New Method for Short Multivariate Fuzzy Time Series Based on Genetic Algorithm and Fuzzy Clustering

Forecasting activities play an important role in our daily life. In recent years, fuzzy time series (FTS) methods were developed to deal with forecasting problems. FTS attracted researchers because of its ability to predict the future values in some critical situations where most standard forecasting models are doubtfully applicable or produce bad fittings. However, some critical issues in FTS are still open; these issues are often subjective and affect the accuracy of forecasting. In this paper, we focus on improving the accuracy of FTS forecasting methods. The new method integrates the fuzzy clustering and genetic algorithm with FTS to reduce subjectivity and improve its accuracy. In the new method, the genetic algorithm is responsible for selecting the proper model. Also, the fuzzy clustering algorithm is responsible for fuzzifying the historical data, based on its membership degrees to each cluster, and using these memberships to defuzzify the results. This method provides better forecasting accuracy when compared with other extant researches

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

Ανάπτυξη προτύπου προσομοίωσης για την πρόβλεψη και τη διαχείριση έκτακτων συμβάντων σε δίκτυα αυτοκινητόδρομων

177 σ.Research on road safety has been of great interest to engineers and planners for decades. Regardless of modeling techniques, a serious factor of inaccuracy - in most past studies - has been data aggregation. Nowadays, most freeways are equipped with continuous surveillance systems making disaggregate traffic data readily available; these have been used in few studies. In this context, the main objective of this dissertation is to capitalize highway traffic data collected on a real-time basis at the moment of accident occurrence in order to expand previous road safety work and to highlight potential further applications. To this end, we first examine the effects of various traffic parameters on type of road crash as well as on the injury level sustained by vehicle occupants involved in accidents, while controlling for environmental and geometric factors. Probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de-France region, France. Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Increased traffic volume is found to have a consistently positive effect on severity, while speed has a differential effect on severity depending on flow conditions. We then establish a conceptual framework for incident management applications using real-time traffic data on urban freeways. We use dissertation previous findings to explore potential implications towards incident propensity detection and enhanced management.Η Οδική Ασφάλεια αποτελεί πεδίο ερευνητικού ενδιαφέροντος για μηχανικούς κατά τις τελευταίες δεκαετίες. Ανεξάρτητα από τις εφαρμοζόμενες μεθόδους προτυποποίησης, σημαντικός παράγοντας ανακρίβειας πρότερων διερευνήσεων είναι η ομαδοποίηση δεδομένων. Ωστόσο, οι περισσότεροι αυτοκινητόδρομοι είναι πλέον εξοπλισμένοι με συστήματα παρακολούθησης, τα οποία καθιστούν διαθέσιμα μη ομαδοποιημένα κυκλοφοριακά δεδομένα. Η διαθεσιμότητα των δεδομένων αυτών δεν έχει επαρκώς αξιοποιηθεί ερευνητικά. Στόχος της διατριβής είναι η αξιοποίηση των κυκλοφοριακών δεδομένων αυτοκινητόδρομων που συλλέγονται σε πραγματικό χρόνο κατά τη στιγμή εκδήλωσης ατυχήματος. Για το σκοπό αυτό, μελετήθηκε η επίδραση διάφορων κυκλοφοριακών παραμέτρων στον τύπο οδικού ατυχήματος, αλλά και στο επίπεδο σοβαρότητας τραυματισμού των επιβαινόντων. Παράλληλα, ελήφθησαν υπόψιν παράγοντες σχετιζόμενοι με το περιβάλλον και τη γεωμετρία. Εφαρμόστηκαν μοντέλα probit σε τετραετή δεδομένα συμβάντων από το κοινό τμήμα των αυτοκινητόδρομων Α4-Α86 στην περιοχή Ile-de-France της Γαλλίας. Τα εμπειρικά αποτελέσματα καταδεικνύουν ότι ο τύπος ατυχήματος μπορεί –σχεδόν αποκλειστικά- να εκτιμηθεί από τις επικρατούσες κυκλοφοριακές συνθήκες. η αύξηση του κυκλοφοριακού φόρτου φαίνεται να ασκεί σταθερή επίδραση στη σοβαρότητα των ατυχημάτων, ενώ η επίδραση της ταχύτητας διαφοροποιείται ανάλογα με το επίπεδο του κυκλοφοριακού φόρτου. Στη συνέχεια, αναπτύσσεται πλαίσιο για την ένταξη κυκλοφοριακών δεδομένων πραγματικού χρόνου στη διαχείριση συμβάντων. Τέλος, τα πορίσματα της διατριβής χρησιμοποιούνται στη διερεύνηση εφαρμογών με απώτερο στόχο τον περιορισμό της προδιάθεσης πρόκλησης συμβάντων και τη βελτιωμένη διαχείρισή τους.Ζωή Δ. Χριστοφόρο

A new approach based on artificial neural networks for high order multivariate fuzzy time series

Fuzzy time series methods have been recently becoming very popular in forecasting. These methods can be categorized into two subclasses that are univariate and multivariate approaches. It is a known fact that real time series data can actually be affected by many factors. In this case, the using multivariate fuzzy time series forecasting model can be more reasonable in order to get more accurate forecasts. To obtain fuzzy forecasts when multivariate fuzzy time series approach is adopted, the most applied method is using tables of fuzzy relations. However, employing this method is a computationally though task. In this study, we introduce a new method that does not require using fuzzy logic relation tables in order to determine fuzzy relationships. Instead, a feed forward artificial neural network is employed to determine fuzzy relationships. The proposed method is applied to the time series data of the total number of annual car road accidents casualties in Belgium from 1974 to 2004 and a comparison is made between our proposed method and the methods proposed by Jilani and Burney [Jilani, T. A., & Burney, S. M. A. (2008). Multivariate stochastic fuzzy forecasting models. Expert Systems with Applications, 35, 691-700] and Lee et al. [Lee, L.-W., Wang, L.-H., Chen, S.-M., & Leu, Y.-H. (2006). Handling forecasting problems based on two factors high order fuzzy time series. IEEE Transactions on Fuzzy Systems, 14, 468-477]