Técnicas de lógica difusa en la predicción de índices de mercados de valores: una revisión de literatura.

El pronóstico de índices de mercados de valores es una tarea importante en ingeniería financiera, porque es una información necesaria para la toma de decisiones. Este estudio tiene como objetivo evaluar el estado del arte en el progreso del pronóstico del mercado de valores, usando metodologías basadas en sistemas de inferencia borrosa y redes neuronales neuro-difusas, enfatizando el caso del Índice General de la Bolsa de Colombia (IGBC). Se empleó la revisión sistemática de literatura para responder cuatro preguntas de investigación. Existe una tendencia importante sobre el uso de las metodologías basadas en inferencia difusa para predecir los índices de los mercados de valores, explicada por la precisión del pronóstico en comparación con otras metodologías tradicionales. La mayoría de las investigaciones se enfocan en metodologías de “series de tiempo difusas” y ANFIS, pero, hay otras aproximaciones prometedoras que no han sido evaluadas aún. Existe un vacío de investigación en el caso del mercado accionario colombiano

SISTEM INFORMASI PERAMALAN ANGKA KEJADIAN PENYAKIT DEMAM BERDARAH MENGGUNAKAN MULTIVARIATE FUZZY TIME SERIES

Indonesia merupakan negara dengan iklim tropis yang menyebabkan terjadinya dua musim, penghujan dan kemarau. DB atau Demam Berdarah Dengue merupakan penyakit yang biasanya menyerang pada musim penghujan. Namun tidak menutup kemungkinan Demam Berdarah juga menyerang pada musim kemarau. Kabupaten Demak merupakan salah satu daerah di Provinsi Jawa Tengah dengan angka kejadian Demam Berdarah yang cukup rendah dibandingkan dengan kota dan kabupaten lain. Meskipun begitu, pengendalian Demam Berdarah perlu dilakukan untuk meminimalisir terjadinya lonjakan angka kejadian Demam Berdarah, karena Demam Berdarah merupakan penyakit yang cukup berbahaya. Salah satu bentuk pengendalian angka kejadian DB yang banyak digunakan yaitu menggunakan model peramalan, salah satunya yaitu menggunakan Fuzzy Time Series. Model Multivariate Fuzzy Time Series (MFTS) merupakan pengembangan dari model Fuzzy Time Series yang dapat digunakan untuk melakukan peramalan dengan menggunakan data time series dengan menggunakan lebih dari satu variabel untuk peramalan, dibandingkan dengan metode Fuzzy Time Series biasanya hanya menggunakan satu variabel saja. Data aktual yang digunakan untuk peramalan berupa angka kejadian Demam Berdarah, curah hujan dan hari hujan dari bulan Januari 2013 hingga Desember 2018, dengan skenario peramalan 2 tahun training dan testing, 3 tahun training dan testing, 6 tahun training dan testing. Berdasarkan hasil penelitian yang didapat, model MFTS memiliki nilai MAPE yang rata-rata menghasilkan nilai peramalan yang cukup akurat, dengan nilai MAPE terendah, ada pada skenario 3 tahun pada orde 5 dengan MAPE 10,394%. Kata kunci: Demam Berdarah, Multivariate Fuzzy Time Series, Fuzzy Time Series Indonesia is a country with a tropical climate that causes two seasons, the rainy season and the dry season. DHF or Dengue Hemorrhagic Fever is a disease that usually attacks during the rainy season. But it does not rule out DHF also attacking in the dry season. Demak Regency is one of the regions in Central Java Province with a low incidence of Dengue Fever compared to other cities and districts. Even so, DHF control needs to be done to minimize the occurrence of dengue fever, because DHF is a fairly dangerous disease. One form of controlling the number of DHF events that is widely used is using forecasting models, one of which is using Fuzzy Time Series. The Multivariate Fuzzy Time Series (MFTS) model is a development of the Fuzzy Time Series model that can be used to forecast using time series data by using more than one variable for forecasting, compared to the Fuzzy Time Series method usually using only one variable. The actual data used for forecasting are DHF incidence rates, rainfall and rainy days from January 2013 to December 2018, with a forecast scenario of 2 years of training and testing, 3 years of training and testing, 6 years of training and testing. Based on the research results obtained, the MFTS model has an MAPE value that on average produces a fairly accurate forecasting value, with the lowest MAPE value, there is a scenario of 3 years in order 5 with a MAPE of 10.394%. Keywords: Dengue Fever, Multivariate Fuzzy Time Series, Fuzzy Time Serie

ЕКОНОМІКО-МАТЕМАТИЧНЕ МОДЕЛЮВАННЯ ПРОЦЕСІВ ОПТИМІЗАЦІЇ ЛОГІСТИКИ ЦУКРОВОЇ СИРОВИНИ НА ЦУКРОВИХ ЗАВОДАХ

The mathematical model of proceeding in the supplies of agricultural on a factory-producer under force-majeure circumstances of their deficit products is suggested. It enables bringing in to the supply of feedstock not only own sources but also alternative ones.  Small and middle farmers became such suppliers. This helps to reduce the risk of failures in raw materials’ supply to sugar factories, as well as to integrate the interests of large associations of sugar producers with the interests of small and medium producers. This also helps to solve the tasks of socio-economic development of rural areas as it provides sales to small and medium agroproducers.The mathematical formulation of the problem is complicated by the presence of probabilistic parameters. Therefore, in the case of mathematical modeling, a stochastic network model was used.The complexity of the task of organizing the supply of raw materials by small and middle producers is due to the fact that the set of sugar beet suppliers is an open heterogeneous system, and the consumer is a link of a centralized association with a hierarchical structure.The factor of heterogeneity of the suppliers’ set is due to the fact that each of them acts autonomously and even has no information about the actions of other suppliers.The mathematical model allows to predict the time of implementation of the project and to solve the task concerning delivery of raw materials of the quantity required, for a given period of time, from a substantial number of autonomous suppliers. The developed mathematical model has allowed to take into account the influence of probabilistic factors on the decisive factor - the timely organization and restoration of the necessary resource for the smooth operation of sugar production.Independent substructures, by definition, do not have access to the centralized network schedule of the producer. In order to resolve this contradiction, the following decision is taken. For each of the autonomous primary producers, their own network schedule is created.The developed mathematical model can be used for other cases of emergent stocks replenishment, urgent formation of commodity batches of agricultural products.Предложена экономико-математическая модель восстановления запасов сельскохозяйственной продукции на заводе-изготовителе при форс-мажорных обстоятельствах их дефицита. Это дает возможность привлечения к поставкам сырья не только из собственных источников, но и из альтернативных. Такими поставщиками становятся малые и средние производители. Это позволяет уменьшить риски срывов поставок сырья на сахарные заводы, а также интегрировать интересы крупных ассоциаций производителей сахара с интересами малого и среднего производителя.Математическая постановка задачи осложнена наличием вероятностных параметров. Поэтому при экономико-математическом моделировании использована стохастическая сетевая модель.Разработанная экономико-математическая модель может быть использована для других случаев экстренного пополнения запасов, срочного формирования товарных партий сельскохозяйственной продукции.Запропоновано математичну модель відновлення запасів сільськогосподарської продукції на заводі-виробнику за форс-мажорних обставин їх дефіциту. Це дає можливість залучення до постачання сировини не тільки з власних, а й з альтернативних джерел. Такими постачальниками стають малі та середні виробники. Це дозволяє зменшити ризики зривів постачання сировини на цукрові заводи а також інтегрувати інтереси великих асоціацій виробників цукру з інтересами малого та середнього виробника. Це також допомагає ви-рішити задачі соціально-економічного розвитку села бо забезпечує збут для малих та серед-ніх виробників агропродукції.Математична постановка задачі ускладнена наявністю імовірнісних параметрів. Тому при математичному моделюванні використана стохастична мережева модель.Складність задачі організації постачання сировини від малих та середніх виробників обумовлена тим, що сукупність постачальників цукрового буряку є відкритою гетерогенною системою, а споживач є ланкою централізованої асоціації з ієрархічною структурою.Фактор гетерогенності сукупності постачальників обумовлений тим, що кожен з постачальників сировини діє автономно і навіть не має інформації про дії інших постачальників.Математична модель дозволяє прогнозувати час виконання проекту і вирішити здачу постачання  сировини в потрібній кількості у заданий термін від значної кількості автономних постачальників. Розроблена математична модель, дозволила забезпечити врахування впливу імовірнісних факторів на вирішальний чинник – своєчасну організацію та відновлен-ня необхідного ресурсу для безперебійної роботи цукрового виробництваАвтономні субструтури, за визначенням,  не мають доступу до централізованого мережевого графіку виробника. Задля вирішення цього протиріччя приймається наступне рішен-ня. Для кожного з автономних постачальників формується власний мережевий графік.Розроблена математична модель може бути використана для інших випадків екстреного поповнення запасів, термінового формування товарних партій сільськогосподарської продукції

Identifying Outliers in Fuzzy Time Series

Time series analysis is often associated with the discovery of patterns and prediction of features. Forecasting accuracy can be improved by removing identified outliers in the data set using the Cook’s distance and Studentized residual test. In this paper a modified fuzzy time series method is proposed based on transition probability vector membership function. It is experimentally shown that the proposed method minimizes the average forecasting error compared with other known existing methods

Modelling and predicting energy consumption in laboratory buildings using multiple linear regression

This study was carried to improve the energy saving by investigating the influence factors that contribute to high energy consumption in a building particularly related to the building in Technology Campus, UTeM. Correlation analysis was performed to measure the strength of relationship between the influence factors whereby all the factors proven to have a strong linear correlation with the energy consumption. The Stepwise Selection of Multiple Linear Regression (MLR) were used to determine and modelling the most influence factors that affects the energy consumption. The final linear regression models was developed based on the amount of lighting in a building and surrounding temperature in the building which is considered as major influence factors that affect the energy consumption. Comparing the actual and predicted energy consumption in Technology Campus, UTeM showed that the MLR model obtained can be used to predict energy consumption and accounted for around 81% of the variance

Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

Many models and techniques have been proposed by researchers to improve forecasting accuracy using fuzzy time series. However, very few studies have tackled problems that involve inverse fuzzy function into fuzzy time series forecasting. In this paper, we modify inverse fuzzy function by considering new factor value in establishing the forecasting model without any probabilistic approaches. The proposed model was evaluated by comparing its performance with inverse and non�inverse fuzzy time series models in forecasting the yearly enrollment data of several universities, such as Alabama University, Universiti Teknologi Malaysia (UTM), and QiongZhou University; the yearly car accidents in Belgium; and the monthly Turkish spot gold price. The results suggest that the proposed model has potential to improve the forecasting accuracy compared to the existing inverse and non-inverse fuzzy time series models. This paper contributes to providing the better future forecast values using the systematic rules. Keywords: Fuzzy time series, inverse fuzzy function, non-probabilistic model, non-inverse fuzzy model, future forecas

Learning Curve as a Knowledge-based Dynamic Fuzzy Set: A Markov Process Model

In the fuzzy set theory introduced by Zadeh (1965), membership de-gree of a fuzzy set is determined by a static membership function, i.e., it does not change over time. To improve this condition then Wang introduced the dy-namic fuzzy logic. In this concept, the membership degree of a fuzzy set is changing over the time. Intan and Mukaido (2002) introduced the knowledge-based fuzzy set, by means that the membership degree of a set is dependent on the knowledge of a person. Since the knowledge of a person is not static, the knowledge-based fuzzy set can be measured dynamically over time, so that we have the knowledge-based dynamic fuzzy set. In this paper, we approximate the learning process as a knowledge-based dynamic fuzzy set. We consider that the process of learning is dependent on the knowledge of a person from time to time so that we can model the learning process is a Markov process of dynamic knowledge. Additionally, using the triangular fuzzy number, we follow Yabuu-chi et al. (2014), for modelling the time difference in the dynamic knowledge fuzzy set as an autoregressive model of order one

A Fuzzy Time Series-Based Model Using Particle Swarm Optimization and Weighted Rules

During the last decades, a myriad of fuzzy time series models have been proposed in scientific literature. Among the most accurate models found in fuzzy time series, the high-order ones are the most accurate. The research described in this paper tackles three potential limitations associated with the application of high-order fuzzy time series models. To begin with, the adequacy of forecast rules lacks consistency. Secondly, as the model's order increases, data utilization diminishes. Thirdly, the uniformity of forecast rules proves to be highly contingent on the chosen interval partitions. To address these likely drawbacks, we introduce a novel model based on fuzzy time series that amalgamates the principles of particle swarm optimization (PSO) and weighted summation. Our results show that our approach models accurately the time series in comparison with previous methods

A Fuzzy Time Series-Based Model Using Particle Swarm Optimization and Weighted Rules

During the last decades, a myriad of fuzzy time series models have been proposed in scientific literature. Among the most accurate models found in fuzzy time series, the high-order ones are the most accurate. The research described in this paper tackles three potential limitations associated with the application of high-order fuzzy time series models. To begin with, the adequacy of forecast rules lacks consistency. Secondly, as the model's order increases, data utilization diminishes. Thirdly, the uniformity of forecast rules proves to be highly contingent on the chosen interval partitions. To address these likely drawbacks, we introduce a novel model based on fuzzy time series that amalgamates the principles of particle swarm optimization (PSO) and weighted summation. Our results show that our approach models accurately the time series in comparison with previous methods

Pronóstico del Índice General de la Bolsa de Valores de Colombia (IGBC) usando modelos de inferencia difusa

Resumen: El pronóstico de índices de mercados de valores es un insumo necesario para tomar decisiones adecuadas de inversión. En este sentido, estudios recientes han señalado la influencia de los indicadores de los principales mercados bursátiles y de otros indicadores económicos sobre los índices de los mercados emergentes. El primer objetivo de este trabajo es determinar si el valor esperado de los rendimientos logarítmicos del Índice General de la Bolsa (IGBC) puede ser explicado por el comportamiento de los rendimientos logarítmicos del S and P500, NASDAQ, el precio del petróleo WTI y la tasa representativa del mercado. El segundo objetivo es comparar la precisión del pronóstico cuando se consideran los siguientes tipos de modelos: regresión lineal múltiple, ANFIS, Hyfis y redes neuronales autorregresivas con variables explicativas. Los resultados muestran que el pronóstico más preciso es obtenido con una red neuronal autorregresiva que usa como entradas el NASDAQ, el S and P500,el precio del petróleo WTI, las interacciones del NASDAQ, el S and P500 y el precio del petróleo WTI con la tasa representativa del mercado y las interacciones del NASDAQ y el S and P500 con el precio del petróleo WTI . Además se concluye que la influencia de las variables explicativas sobre el índice no es linealAbstract: In this article, the daily Colombian exchange market index (IGBC) is forecasted using linear models, artificial neural networks and adaptive neuro-fuzzy inference systems with the aim of evaluate the accuracy of the forecasts when nonlinear models are used.In addition, we evaluate the explanatory power of other international market indexes, oil prices and exchange rates. Our findings are the following: first, an autoregressive neural network better captures the behavior of the IGBC in comparison with linear and adaptive neuro-fuzzy models; second, the preferred explanatory variables are able to explain complex properties as heteroskedasticity and non-normality of the residuals. And third, it is necessary consider as inputs not only the explanatory variables alone but also their interactionsMaestrí