Abstract
The Indonesian Stock Exchange (IDX) stock market index is one of the main indicators commonly used as a reference for national economic conditions. The value of the stock market index is often being used by investment companies and individual investors to help making investment decisions. Therefore, the ability to predict the stock market index value is a critical need. In the fields of statistics and probability theory as well as machine learning, various methods have been developed to predict the value of the stock market index with a good accuracy. However, previous research results have found that no one method is superior to other methods. This study proposes an ensemble model based on deep learning architecture, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), called the CNN-LSTM. To be able to predict financial time series data, CNN-LSTM takes feature from CNN for extraction of important features from time series data, which are then integrated with LSTM feature that is reliable in processing time series data. Results of experiments on the proposed CNN-LSTM model confirm that the hybrid model effectively provides better predictive accuracy than the stand-alone time series data forecasting models, such as CNN and LSTM.
Highlights
The Indonesian Stock Exchange (IDX) stock market index is one of the main indicators commonly used as a reference for national economic conditions
“Stock prediction model based on neural network,” Operations Research and Management Science, vol 28, no. 10, pp. 132–140, 2019
Dalam uji coba digunakan data indices based on deep Long Short-Term Memory (LSTM) neural network,” Statistical Research, vol 36, no. 6, pp. 65–77, 2019
Summary
Selain beberapa model peramalan data deret waktu dari bidang ilmu statistika dan teori probabilitas di atas, dalam bidang ilmu machine learning khususnya jaringan saraf tiruan juga telah banyak dikembangkan banyak model pembelajaran deep learning untuk pemodelan dan prediksi data deret waktu [12]. Convolutional Neural Network atau CNN yang Guna merealisasikan tujuan penelitian dan juga merupakan salah satu model pembelajaran deep menjawab rumusan masalah yang telah ditetapkan, learning yang telah digunakan pada beberapa penelitian maka tahapan-tahapan yang akan dilakukan dalam sebelumnya untuk melakukan prediksi data deret waktu penelitian ini dijabarkan pada Gambar 1. Awareness of Problem, melakukan kajian model deret waktu dengan mengintegrasikan CNN dan LSTM, ensemble CNN-LSTM untuk diterapkan guna membantu atau membangun model ensemble yang disebut sebagai proses pemodelan dan prediksi pergerakan data deret CNN-LSTM. CNN lebih sering diaplikasi pada data citra, namun model ini juga telah terbukti dapat diterapkan secara efektif pada peramalan data deret waktu [16]. Karakteristik utama dari CNN adalah kemampuan untuk dapat mengenali dan mengekstrak berbagai fitur yang tampak jelas dari garis pandang, sehingga model ini sangat umum digunakan dalam proses rekayasa fitur khususnya ekstraksi fitur dari data
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