Organic Farming in Europe by 2010: Scenarios for the future

How will organic farming in Europe evolve by the year 2010? The answer provides a basis for the development of different policy options and for anticipating the future relative competitiveness of organic and conventional farming. The authors tackle the question using an innovative approach based on scenario analysis, offering the reader a range of scenarios that encompass the main possible evolutions of the organic farming sector. This book constitutes an innovative and reliable decision-supporting tool for policy makers, farmers and the private sector. Researchers and students operating in the field of agricultural economics will also benefit from the methodological approach adopted for the scenario analysis

A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.

"A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?"

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.

PREDIKSI CURAH HUJAN DENGAN MENGGUNAKAN FUZZY FORECASTING BERBASIS AUTOMATIC CLUSTERING DAN AXIOMATIC FUZZY SET CLASSIFICATION

Dalam penelitian ini dilakukan prediksi curah hujan di provinsi Kalimantan Selatan. Pemodelan matematika yang digunakan untuk memprediksi curah hujan yaitu fuzzy forecasting. Dalam memprediksi curah hujan, fuzzy forecasting memiliki empat langkah yang akan menghasilkan nilai prediksi. Langkah-langkah dalam fuzzy forecasting yaitu automatic clustering, Rancang tren fuzzy berlabel training dataset, axiomatic fuzzy set classification, dan forecasting. Dengan menggunakan langkah-langkah fuzzy forecasting tersebut metode ini menghasilkan nilai RMSE sebesar 52.55 dan MAPE sebesar 42.46. Kata kunci : prediksi curah hujan, Automatic Clustering, Axiomatic Fuzzy Set classification

Forecasting Inflation In Indonesia Using The Modified Fuzzy Time Series Cheng

Inflation is one of the most important indicators to analyze a country’s economy. Therefore, it is necessary to forecast the inflation rate. Forecasting can be done by various methods, one of which is Fuzzy Time Series Cheng. In this study, several modifications were made to the method used. The purpose of this study is to forecast using the Modified Fuzzy Time Series (FTS) Cheng method and determine the accuracy of the forecasting results obtained. The results of this study indicate that the Modified FTS Cheng method can be used in forecasting, either by determining the interval average-based or using the Sturges equation. Based on the results of the calculation of forecasting accuracy using Mean Absolute Percentage Error (MAPE), the accuracy for Modified FTS Cheng by determining the average-based interval for forecasting based on the current state and next state is 11.58% and 5.78%, respectively. Furthermore, the Modified FTS Cheng by determining the interval using the Sturges equation resulted in a MAPE value of 9.61% and a FTS Cheng of 7.54%. The MAPE value of each method is less than 10%, which means that the method has a very good performance, except for Modified FTS Cheng by determining the average-based interval for forecasting based on current state has good performance with MAPE values ​​between 10 % and 20%.  Inflasi merupakan salah satu indikator penting yang digunakan dalam menganalisa perekonomian di suatu negara. Oleh karena itu, perlu dilakukan peramalan terhadap tingkat inflasi. Peramalan dapat dilakukan dengan berbagai metode, salah satunya Fuzzy Time Series Cheng. Pada penelitian ini dilakukan beberapa modifikasi pada metode yang digunakan. Tujuan penelitian ini adalah melakukan peramalan menggunakan metode Fuzzy Time Series Cheng yang Dimodifikasi dan menentukan akurasi dari hasil peramalan yang diperoleh. Hasil dari penelitian ini menunjukkan bahwa metode Fuzzy Time Series Cheng Dimodifikasi dapat digunakan dalam melakukan peramalan, baik dengan penentuan interval berbasis rata-rata maupun menggunakan persamaan Sturges. Berdasarkan hasil perhitungan keakuratan peramalan menggunakan Mean Absolute Percentage Error (MAPE) diperoleh akurasi untuk Fuzzy Time Series Cheng Dimodifikasi dengan penentuan interval berbasis rata-rata untuk peramalan berdasarkan current state dan next state masing-masing sebesar 11,58% dan 5,78%. Selanjutnya, Fuzzy Time Series Cheng Dimodifikasi dengan penentuan interval meggunakan persamaan Sturges menghasilkan nilai MAPE sebesar 9,61% dan Fuzzy Time Series Cheng sebesar 7,54%. Nilai MAPE dari masing-masing metode kurang dari 10% yang berarti bahwa metode tersebut mempunyai kinerja yang sangat baik, kecuali Fuzzy Time Series Cheng Dimodifikasi dengan penentuan interval berbasis rata-rata untuk peramalan berdasarkan current state mempunyai kinerja yang baik dengan nilai MAPE berada antara 10% dan 20%

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

"The connection between distortion risk measures and ordered weighted averaging operators"

Distortion risk measures summarize the risk of a loss distribution by means of a single value. In fuzzy systems, the Ordered Weighted Averaging (OWA) and Weighted Ordered Weighted Averaging (WOWA) operators are used to aggregate a large number of fuzzy rules into a single value. We show that these concepts can be derived from the Choquet integral, and then the mathematical relationship between distortion risk measures and the OWA and WOWA operators for discrete and nite random variables is presented. This connection oers a new interpretation of distortion risk measures and, in particular, Value-at-Risk and Tail Value-at-Risk can be understood from an aggregation operator perspective. The theoretical results are illustrated in an example and the degree of orness concept is discussed.Fuzzy systems; Degree of orness; Risk quantification; Discrete random variable JEL classification:C02,C60