Economic Turning Point Forecasting Using the Fuzzy Neural Network and Non-overlap Area Distribution Measurement Method
نویسندگان
چکیده
This paper proposes a new forecasting model based on the neural network with weighted fuzzy membership functions (NEWFM) concerning forecasting of turning points in the business cycle by the composite index. NEWFM is a new model of neural networks to improve forecasting accuracy by using self adaptive weighted fuzzy membership functions. The locations and weights of the membership functions are adaptively trained, and then the fuzzy membership functions are combined by the bounded sum. To simplify the forecasting processes, the non-overlap area distribution measurement method is applied to select important features by deleting less important inputs. The implementation of the NEWFM demonstrates an excellent capability in the field of business cycle analysis.
منابع مشابه
Financial Time-Series Forecasting based on a Neural Network with Weighted Fuzzy Membership Functions and the Takagi-Sugeno Fuzzy Model
This paper proposes financial time-series forecasting using a feature selection method based on the non-overlap area distribution measurement method supported in a neural network with weighted fuzzy membership functions (NEWFM) and the TakagiSugeno (T-S) fuzzy model. The non-overlap area distribution measurement method selects the minimum number of features with the highest performance by remov...
متن کاملForecasting Stock Market Using Wavelet Transforms and Neural Networks: An integrated system based on Fuzzy Genetic algorithm (Case study of price index of Tehran Stock Exchange)
The jamor purpose of the present research is to predict the total stock market index of Tehran Stock Exchange, using a combined method of Wavelet transforms, Fuzzy genetics, and neural network in order to predict the active participations of finance market as well as macro decision makers.To do so, first the prediction was made by neural network, then a series of price index was decomposed by w...
متن کاملShort Term Load Forecasting by Using ESN Neural Network Hamedan Province Case Study
Abstract Forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. This paper studies load consumption modeling in Hamedan city province distribution network by applying ESN neural network. Weather forecasting data such as minimum day temperature, average day temp...
متن کاملA Neural-Network Approach to the Modeling of the Impact of Market Volatility on Investment
In recent years, authors have focused on modeling and forecasting volatility in financial series it is crucial for the characterization of markets, portfolio optimization and asset valuation. One of the most used methods to forecast market volatility is the linear regression. Nonetheless, the errors in prediction using this approach are often quite high. Hence, continued research is conducted t...
متن کاملLong-term Streamflow Forecasting by Adaptive Neuro-Fuzzy Inference System Using K-fold Cross-validation: (Case Study: Taleghan Basin, Iran)
Streamflow forecasting has an important role in water resource management (e.g. flood control, drought management, reservoir design, etc.). In this paper, the application of Adaptive Neuro Fuzzy Inference System (ANFIS) is used for long-term streamflow forecasting (monthly, seasonal) and moreover, cross-validation method (K-fold) is investigated to evaluate test-training data in the model.Then,...
متن کامل