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.

Application of the Machine Learning Method for Predicting International Tourists in West Java Indonesia Using the Averege-Based Fuzzy Time Series Model

Machine learning is a branch of artificial intelligence where machines are designed to learn on their own without human direction. The machine learning method used by data science is one of them for the prediction process, such as predicting the number of tourists. Tourism is one of the economic sectors that has a direct impact on the people's economy. Based on data from the Central Statistics Agency (BPS), the number of tourists coming to West Java, Indonesia fluctuates, meaning that the number can increase and decrease every month and year. This fluctuating change in the number of tourists has an impact on tourism actors. Therefore we need an appropriate model to make predictions so that related parties, one of which is the local government, can make policies in this sector. The purpose of this study is to propose whether the average-based fuzzy time series model is appropriate for use in predicting the number of foreign tourists coming to West Java, Indonesia. In this study the method used for prediction is the fuzzy time series method and the average-length-based algorithm as a determinant of the length of the interval. The effective interval length can affect the prediction results with a higher level of accuracy. The data used in this study is data from foreign tourists who came to West Java from January 2017 to April 2020 from Badan Pusat Statistik  (BPS) of West Java Indonesia. Based on the results of the prediction test, the Mean Absolute Percentage Error (MAPE) value is 14.71%, the results show that the average-based fuzzy time series model is good for prediction. This can be a decision support for related parties to make policies related to tourism preparation and planning efforts in West Java, Indonesia

Data Science: Measuring Uncertainties

With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems

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

Data mining as a tool for environmental scientists

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous