Bankruptcy prediction has been addressed by many researchers in the field of finance since few decades. One of the best approaches to deal with this issue is considering it as a classification problem. In this paper a time series prediction model of bankruptcy via Adaptive neuro-fuzzy inference system (ANFIS) is formulated, which is capable of predicting the bankruptcy of a firm for any future ...

The underground economy (UE) also known as the shadow economy is a phenomenon known throughout the world. The growth of the UE is a matter of increasing concern, at least, throughout the developed nations. Researchers have recognized that the underground economy and crime are linked. Studies on the US, Canada and the transition economies indicate that crime and the shadow economy are facts of l...

Traditionally, the autoregressivemoving average (ARMA)model has been one of themost widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superior...

Fuzzy Time Series (FTS) adalah metode peramalan yang digunakan untuk mengolah data aktual dibentuk ke dalam nilai-nilai linguistik. Salah satu FTS yaitu berbasis Algoritma Novel. Novel merupakan perkembangan dari sebelumnya dimana pada langkah peramalannya menggunakan kecenderungan menentukan nilai peramalannya. Metode ini akan diaplikasikan Indeks Harga Konsumen (IHK) Kota Samarinda periode Ja...

This paper addresses the bullwhip effect in a multi-stage supply chain, where all demands, lead times, and ordering quantities are fuzzy. To simulate the bullwhip effect, a modified Hong Fuzzy Time Series is presented by adding a Genetic Algorithm (GA) module for gaining of a window basis. Next, a back propagation neural network is used for defuzzification. The model can forecast the trends in ...

In this paper mathematical methods for prediction of uncertain structural responses with the aid of fuzzy time series are presented. Uncertain measurments of structural loads and responses respectively at equally spaced discrete time points are modeled as fuzzy variables. Hence uncertain measurments over time are considered as time series with fuzzy data. The fuzzy variables are processed on th...

Fuzzy information granulation transfers the time series analysis from numerical platform to granular platform, which enables us study at a different granularity. In previous studies, each fuzzy granule in can reflect average, range, and linear trend characteristics of data corresponding window. order get more general granule, this paper proposes polynomial granules, both nonlinear The distance ...

In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficient ...