PERAMALAN HARGA SAHAM MENGGUNAKAN RECURRENT NEURAL NETWORK DENGAN ALGORITMA BACKPROPAGATION THROUGH TIME (BPTT)

作者： Setiawardhana Setiawardhana , Linda Aqnes Desi Susanti , Arna Fariza

DOI:

关键词: Artificial neural network 、 Stock price forecasting 、 Set (abstract data type) 、 Backpropagation through time 、 Recurrent neural network 、 Stock exchange 、 Machine learning 、 Network architecture 、 Graph (abstract data type) 、 Computer science 、 Artificial intelligence

摘要: In this final project will be made an application to stock price forecasting using RNN - BPTT. modeling the input data is Close Price of shares on Indonesia Stock Exchange (BEI). Then these time series with neural network algorithms recurrent that BPTT algorithm where architecture used Jordan's RNN. Backpropagation Through Time (BPTT) a fairly popular training for network. there are several feedback loops in connection graph. The main concept spread by putting same copy and manage connections back get between next set. To produce accurate forecasting, parameters tested, such as learning rate, number neurons data. We make expected help investors predict fluctuations so they able determine investment policy future good results. Keywords: Forecasting Series, Price, Recurrent Neural Network,

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PERAMALAN HARGA SAHAM MENGGUNAKAN RECURRENT NEURAL NETWORK DENGAN ALGORITMA BACKPROPAGATION THROUGH TIME (BPTT)

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PERAMALAN HARGA SAHAM MENGGUNAKAN RECURRENT NEURAL NETWORK DENGAN ALGORITMA BACKPROPAGATION THROUGH TIME (BPTT)

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