scholarly journals Forecasting Financial Time Series Using Multiple Regression, Multi Layer Perception, Radial Basis Function and Adaptive Neuro Fuzzy Inference System Models: A Comparative Analysis

2012 ◽  
Vol 5 (6) ◽  
Author(s):  
Arindam Chaudhuri
Keyword(s):  
Time Series ◽  
Comparative Analysis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Financial Time Series ◽  
Inference System ◽  
Financial Time ◽  
Neuro Fuzzy

scholarly journals HYBRID MODEL OF SELF-ORGANIZING MAP AND ADAPTIVE NEURO FUZZY INFERENCE SYSTEM IN STOCK INDEXES FORECASTING

2021 ◽  
Vol 9 (2) ◽  
pp. 70-80
Author(s):  
M. Kushnir ◽  
K. Tokarieva
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Prices ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Financial Time Series ◽  
Self Organizing Map ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Self Organizing

The paper investigates methods of artificial intelligence in the prognostication and analysis of financial data time series. It is uncovered that scholars and practitioners face some difficulties in modelling complex system such as the stock market because it is nonlinear, chaotic, multi- dimensional, and spatial in nature, making forecasting a complex process. Models estimating nonstationary financial time series may include noise and errors. The relationship between the input and output parameters of the models is essentially non-linear, where stock prices include higher-level variables, which complicates stock market modeling and forecasting. It is also revealed that financial time series are multidimensional and they are influenced by many factors, such as economics, politics, environment and so on. Analysis and evaluation of multi- dimensional systems and their forecasting should be carried out by machine learning models. The problem of forecasting the stock market and obtaining quality forecasts is an urgent task, and the methods and models of machine learning should be the main mathematical tools in solving the above problems. First, we proposed to use self-organizing map, which is used to visualize multidimensional data by configuring neurons to quantize or cluster the input space in the topological structure. These characteristics of this algorithm make it attractive in solving many problems, including clustering, especially for forecasting stock prices. In addition, the methods discussed, encourage us to apply this cluster approach to present a different data structure for forecasting. Thus, models of adaptive neuro-fuzzy inference system combine the characteristics of both neural networks and fuzzy logic. Given the fact that the rule of hybrid learning and the theory of logic is a clear advantage of adaptive neuro-fuzzy inference system, which has computational advantages over other methods of parameter identification, we propose a new hybrid algorithm for integrating self-organizing map with adaptive fuzzy inference system to forecast stock index prices. This algorithm is well suited for estimating the relationship between historical prices in stock markets. The proposed hybrid method demonstrated reduced errors and higher overall accuracy.

scholarly journals Prediksi Kecepatan Angin Menggunakan Adaptive Neuro Fuzzy (ANFIS) dan Radial Basis Function Neural Network (RBFNN)

2018 ◽  
Vol 4 (1) ◽  
pp. 70
Author(s):  
Nerfita Nikentari ◽  
Martaleli Bettiza ◽  
Helen Sastypratiwi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Radial Basis Function ◽  
Basis Function ◽  
Time Series Data ◽  
Fuzzy Inference ◽  
Series Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Radial Basis

Angin sebagai salah satu fenomena alam yang mempengaruhi berbagai aspek dalam kehidupan manusia baik pengaruh positif maupun negatif. Aspek ini berperan besar dalam ekonomi, pariwisata, pembangunan, transportasi maupun perdagangan masyarakat. Data angin dalam hal ini kecepatan angin belum dapat diketahui secara pasti nilainya oleh karena itu perlu adanya prediksi. Adaptive Neuro Fuzzy Inference System (ANFIS) dan Radial Basis Function Neural Networkc(RBFNN) adalah algoritma yang dapat digunakan untuk prediksi data. Penelitian ini  menggunakan ANFIS dan RBFNN untuk memprediksi kecepatan angin. Data prediksi yang digunakan dalam penelitian ini adalah data time series. Data kecepatan angin diperoleh dari BMKG (Badan Meteorologi Klimatogi dan Geofisika) Tanjungpinang, Kepualuan Riau. Hasil prediksi dengan kedua metode ini dibandingan dengan data asli untuk mengetahui metode mana yang lebih akurat dalam prediksi data. Hasil pengujian menggunakan kedua algoritma memperlihatkan akurasi terbaik (paling mendekati data asli/target) diperoleh oleh RBFNN yaitu dengan nilai RMSE adalah 0,1766 dan hasil RMSE ANFIS adalah 1,1456.

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