An Effective Stock Price Prediction Technique Using Hybrid Adaptive Neuro Fuzzy Inference System Based on Grid Partitioning

2015 ◽  
pp. 275-284 ◽  
Author(s):  
Atanu Chakraborty ◽  
Debojoyti Mukherjee ◽  
Amit Dutta ◽  
Aruna Chakraborty ◽  
Dipak Kumar Kole
Keyword(s):  
Fuzzy Inference System ◽  
Stock Price ◽  
Fuzzy Inference ◽  
Inference System ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Grid Partitioning ◽  
Neuro Fuzzy ◽  
Prediction Technique

scholarly journals IMPLEMENTATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) METHOD ON RICE PRICE PREDICTION IN LUBUKLINGGAU CITY

2021 ◽  
Vol 4 (2) ◽  
pp. 260-269
Author(s):  
Zulfauzi - ◽  
Budi Santoso ◽  
M. Agus Syamsul Arifin ◽  
Siti Nuraisyah
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
City Area ◽  
Price Prediction ◽  
Neuro Fuzzy ◽  
Rice Price ◽  
The Future ◽  
Anfis Method ◽  
Rice Prices

The problem behind this research is the imbalance between the capacity offered and the capacity demanded by the community, resulting in uncontrolled rice prices, so it is necessary to predict rice price in the future to monitor the stability of rice prices in the Lubuklinggau City area. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method was used to predict future rice prices. The sample used in this study is data on rice price in Lubuklinggau City from January 2016 to December 2020. The result of the prediction of rice price in the Lubuklinggau City area for the next five years. With the accuracy value in rice price predictions based on MSE training, numely 99,9037% and based on the MSE test that is 99,8784%. While the accuracy values of rice price predictions based on MAPE training and testing are 93,2997% and 88,2782%, respectively. For the accuracy value of rice price prediction result based on the MSE and MAPE values respectively namely 99,8935% and 92,9212%. It can be concluded that the ANFIS method is very effectively used for the process of predicting a price or value in the future

A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM

2019 ◽  
Vol 18 (01) ◽  
pp. 287-310 ◽  
Author(s):  
Jihong Xiao ◽  
Xuehong Zhu ◽  
Chuangxia Huang ◽  
Xiaoguang Yang ◽  
Fenghua Wen ◽  
...  
Keyword(s):  
Stock Price ◽  
Fuzzy Inference ◽  
Singular Spectrum Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Noise Component ◽  
Inference System ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Three Components

Stock price exhibits distinct features during different time scales due to the effects of complex factors. Analyzing these features can help delineate the mechanisms that determine the stock price and enhance the prediction accuracy of the stock price. By using singular spectrum analysis (SSA), this paper first decomposes the original price series into a trend component, a market fluctuation component and a noise component to analyze the stock price. The economic meanings of the three components are identified as a long-term trend, effects of significant events and short-term fluctuations caused by noise in the market. Then, to take into account the features of the above three components to the stock price prediction, a novel combined model that integrates SSA and support vector machine (SVM) (e.g., SSA–SVM) is proposed. Compared with SVM, adaptive network-based fuzzy inference system (ANFIS), ensemble empirical mode decomposition-ANFIS (EEMD–ANFIS), EEMD–SVM and SSA–ANFIS, SSA–SVM demonstrates the best prediction performance based on four criteria, indicating that the proposed model is a promising approach for stock price prediction.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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