Forecasting Unemployment Rate Using a Neural Network with Fuzzy Inference System

Greece is a low-productivity economy with an ineffective welfare state, relying almost exclusively on low wages and social transfers. Failure to come to terms with this reality hampers both the appropriateness of EU recommendations and the Greek government's capacity to deal with unemployment. Rather than finding a job in a family business or through relationship contacts, young people stay unemployed. Nor can people move back to their village of origin so easily. The underground economy, and the mass of small companies which characterize the Greek economy are booming, on paper. One in three members of the workforce are "self-employed", compared to one in seven in the EU as a whole. (International Viewpoint) An unemployed person in Greece is 2,15 times more likely to suffer poverty than a person in employment. Yet in Greece there are perhaps even more influential factors in determining increased risk of poverty. Thus while unemployment is a crucial factor in the risk of poverty, it is neither the only nor the most significant factor. The paper presents a new technique in the field of unemployment modeling in order to forecast unemployment index. Techniques from the Artificial Neural Networks and from fuzzy logic have been combined to generate a neuro-fuzzy model. The input is a time series. Classical statistics measures are calculated in order to asses the model performance. Further the results are compared with an ARMA and an AR model.forecasting, neural network, unemployment

Forecasting Automobile Demand Via Artificial Neural Networks & Neuro-Fuzzy Systems

The objective of this research is to obtain an accurate forecasting model for the demand for automobiles in Iran\u27s domestic market. The model is constructed using production data for vehicles manufactured from 2006 to 2016, by Iranian car makers. The increasing demand for transportation and automobiles in Iran necessitated an accurate forecasting model for car manufacturing companies in Iran so that future demand is met. Demand is deduced as a function of the historical data. The monthly gold, rubber, and iron ore prices along with the monthly commodity metals price index and the Stock index of Iran are Artificial neural network (ANN) and artificial neuro-fuzzy system (ANFIS) have been utilized in many fields such as energy consumption and load forecasting fields. The performances of the methodologies are investigated towards obtaining the most accurate forecasting model in terms of the forecast Mean Absolute Percentage Error (MAPE). It was concluded that the feedforward multi-layer perceptron network with back-propagation and the Levenberg-Marquardt learning algorithm provides forecasts with the lowest MAPE (5.85%) among the other models. Further development of the ANN network based on more data is recommended to enhance the model and obtain more accurate networks and subsequently improved forecasts

Prediction in Photovoltaic Power by Neural Networks

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches

First-Order ARMA Type Fuzzy Time Series Method Based on Fuzzy Logic Relation Tables

Fuzzy time series approaches have an important deficiency according to classical time series approaches. This deficiency comes from the fact that all of the fuzzy time series models developed in the literature use autoregressive (AR) variables, without any studies that also make use of moving averages (MAs) variables with the exception of only one study (Egrioglu et al. (2013)). In order to eliminate this deficiency, it is necessary to have many of daily life time series be expressed with Autoregressive Moving Averages (ARMAs) models that are based not only on the lagged values of the time series (AR variables) but also on the lagged values of the error series (MA variables). To that end, a new first-order fuzzy ARMA(1,1) time series forecasting method solution algorithm based on fuzzy logic group relation tables has been developed. The new method proposed has been compared against some methods in the literature by applying them on Istanbul Stock Exchange national 100 index (IMKB) and Gold Prices time series in regards to forecasting performance

Integrated computational intelligence and Japanese candlestick method for short-term financial forecasting

This research presents a study of intelligent stock price forecasting systems using interval type-2 fuzzy logic for analyzing Japanese candlestick techniques. Many intelligent financial forecasting models have been developed to predict stock prices, but many of them do not perform well under unstable market conditions. One reason for poor performance is that stock price forecasting is very complex, and many factors are involved in stock price movement. In this environment, two kinds of information exist, including quantitative data, such as actual stock prices, and qualitative data, such as stock traders\u27 opinions and expertise. Japanese candlestick techniques have been proven to be effective methods for describing the market psychology. This study is motivated by the challenges of implementing Japanese candlestick techniques to computational intelligent systems to forecast stock prices. The quantitative information, Japanese candlestick definitions, is managed by type-2 fuzzy logic systems. The qualitative data sets for the stock market are handled by a hybrid type of dynamic committee machine architecture. Inside this committee machine, generalized regression neural network-based experts handle actual stock prices for monitoring price movements. Neural network architecture is an effective tool for function approximation problems such as forecasting. Few studies have explored integrating intelligent systems and Japanese candlestick methods for stock price forecasting. The proposed model shows promising results. This research, derived from the interval type-2 fuzzy logic system, contributes to the understanding of Japanese candlestick techniques and becomes a potential resource for future financial market forecasting studies --Abstract, page iii

Адаптивное кратковременное прогнозирование выбранных финансовых процессов

Запропоновано комп’ютерну систему адаптивного моделювання і прогнозування фінансово-економічних процесів із застосуванням принципів системного аналізу. При цьому враховувалася ієрархічність процесу прийняття рішень при оцінюванні прогнозів, а також застосовувались методи опису і врахування невизначеностей структурного, параметричного і статистичного характеру. Використання взаємодоповнювальних методів оцінювання структури і параметрів математичних моделей, а також оптимального оцінювання станів динамічних систем дає можливість врахувати деякі типи статистичних невизначеностей. Методи імовірнісного моделювання забезпечують урахування невизначеностей імовірнісного типу. Розглянуто задачу короткострокового прогнозування ціни на золото з використанням множини регресійних моделей і фільтра Калмана для отримання оптимальних оцінок стану процесу формування цін. Кращі результати прогнозування отримано з використанням оптимального фільтра за моделями, які враховують авторегресійні складові і тренди процесу. Побудовано моделі умовної дисперсії, які забезпечують прийнятні за якістю оцінки прогнозів дисперсії (волатильності), придатні для прийняття рішень при виконанні торгових операцій на біржі.A computer based system is proposed for adaptive modeling and forecasting of financial and economic processes, that is constructed with application of system analysis principles. A hierarchical structure of decision making process during forecasts estimation was taken into consideration and the methods were used for describing uncertainties of structural, parametric and statistical nature. To estimate model structure and parameters several mutually supporting estimation techniques were used as well as optimal state estimation procedure for dynamic systems that allowed take into consideration some types of structural and statistical uncertainties. Probabilistic modeling methods make it possible to consider uncertainties of probabilistic type. The problem of short term forecasting for gold price is considered as an example using a set of constructed regression models and Kalman filter for generating optimal estimates of states. The best forecasting results were achieved with optimal filter and autoregression models with trends. Also the models were constructed for conditional variance that provided acceptable quality forecasts for variance (volatility) that could be used for constructing decision making rules in trading operations.Предложена компьютерная система для адаптивного моделирования и прогнозирования финансово-экономических процессов, построенная с использованием принципов системного анализа. При этом учитывалась иерархическая структура процесса принятия решений при оценивании прогнозов, а также использовались методы описания неопределенностей структурного, параметрического и статистического характера. Использование взаимодополняющих методов оценивания структуры и параметров математических моделей, а также оптимального оценивания состояний динамических систем позволяет учитывать некоторые типы структурных и статистических неопределенностей. Методы вероятностного моделирования дают возможность учитывать неопределенности вероятностного типа. Рассмотрена задача краткосрочного прогнозирования цены на золото с помощью множества построенных регрессионных моделей и фильтра Калмана. Лучшие оценки прогнозов получены с помощью оптимального фильтра и авторегрессионных моделей с трендами. Построены модели для условной дисперсии, обеспечивающие приемлемые по качеству оценки прогнозов дисперсии (волатильности), которые можно использовать для построения правил принятия решений при выполнении торговых операций на бирже

High-order RTV-FUZZY time series forecasting model based on trend variation

Time series data principally involves four major components which are trend, cyclical, seasonal and irregular, that reflects the characteristics of the data. Ignoring the systematic analysis of patterns from time series components will affect forecasting accuracy. Thus, this paper proposes a high-order ratio trend variation (RTV) fuzzy time series model based on the trend pattern and variations in time series to deal with patterns within the time series data. RTV is used in the fuzzification process to deal with data that contains vagueness, uncertainty and impreciseness. Proper adjustment was also applied to handle the common issues in fuzzy time series model includes determination of length of interval, fuzzy logic relations (FLRs), assigning weight to each FLR and the defuzzification process. Empirical analysis was performed on enrollments data of Alabama University to assess the efficiency of the model. The performance of the proposed model was evaluated by comparing the average forecasting error rate and mean square error values with several fuzzy time series models in the literatures. Experimental results revealed that the proposed model was better than other fuzzy time series models. The use of RTV was able to grip the fuzziness in time series data and reduce the estimation of forecasting errors. In addition, this technique is capable to identify and describe the underlying structure that influence the occurrence of the uncertainty and high fluctuation of the phenomena under investigation

Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China

© 2018 Elsevier B.V. With atmospheric environmental pollution becoming increasingly serious, developing an early warning system for air quality forecasting is vital to monitoring and controlling air quality. However, considering the large fluctuations in the concentration of pollutants, most previous studies have focused on enhancing accuracy, while few have addressed the stability and uncertainty analysis, which may lead to insufficient results. Therefore, a novel early warning system based on fuzzy time series was successfully developed that includes three modules: deterministic prediction module, uncertainty analysis module, and assessment module. In this system, a hybrid model combining the fuzzy time series forecasting technique and data reprocessing approaches was constructed to forecast the major air pollutants. Moreover, an uncertainty analysis was generated to further analyze and explore the uncertainties involved in future air quality forecasting. Finally, an assessment module proved the effectiveness of the developed model. The experimental results reveal that the proposed model outperforms the comparison models and baselines, and both the accuracy and the stability of the developed system are remarkable. Therefore, fuzzy logic is a better option in air quality forecasting and the developed system will be a useful tool for analyzing and monitoring air pollution