A Neural Network and Web-Based Decision Support System for Forex Forecasting and Trading

Lai, Kin Keung; Yu, Lean; Wang, Shouyang

doi:10.1007/978-3-540-30537-8_27

Cited by 19 publications

(7 citation statements)

References 4 publications

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“…Recent achievements have shown that it is also of great use to combine different neural networks into recommender systems. There are many works which employed neural networks to improve the recommendation in the domain of recommender systems [3][4][5][6][7] and in the context of web-based decisions [8][9][10][11][12]. The success of these recommendation approaches based on neural networks demonstrates its capability.…”

Section: Introductionmentioning

confidence: 99%

An Attention-Based Recommender System to Predict Contextual Intent Based on Choice Histories across and within Sessions

et al. 2018

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Recent years have witnessed the growth of recommender systems, with the help of deep learning techniques. Recurrent Neural Networks (RNNs) play an increasingly vital role in various session-based recommender systems, since they use the user’s sequential history to build a comprehensive user profile, which helps improve the recommendation. However, a problem arises regarding how to be aware of the variation in the user’s contextual preference, especially the short-term intent in the near future, and make the best use of it to produce a precise recommendation at the start of a session. We propose a novel approach named Attention-based Short-term and Long-term Model (ASLM), to improve the next-item recommendation, by using an attention-based RNNs integrating both the user’s short-term intent and the long-term preference at the same time with a two-layer network. The experimental study on three real-world datasets and two sub-datasets demonstrates that, compared with other state-of-the-art methods, the proposed approach can significantly improve the next-item recommendation, especially at the start of sessions. As a result, our proposed approach is capable of coping with the cold-start problem at the beginning of each session.

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Section: Introductionmentioning

confidence: 99%

An Attention-Based Recommender System to Predict Contextual Intent Based on Choice Histories across and within Sessions

et al. 2018

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“…Ada beberapa penelitian tentang prediksi forex menggunakan model backpropagation yang telah dilakukan yaitu dari Joarder Kamruzzaman, dkk [10] [11] menyajikan dukungan jaringan & web berbasis pengambilan saraf sistem (DSS) untuk valuta asing (valas) peramalan dan keputusan trading, yang disesuaikan dengan kebutuhan lembaga keuangan dan investor individu. Dalam karya penelitian K.K.…”

Section: Pendahuluanunclassified

Prediksi Forex Menggunakan Model Neural Network

Kusumodestoni

Suyatno

2015

Simet

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ABSTRAKPrediksi adalah salah satu teknik yang paling penting dalam menjalankan bisnis forex. Keputusan dalam memprediksi adalah sangatlah penting, karena dengan prediksi dapat membantu mengetahui nilai forex di waktu tertentu kedepan sehingga dapat mengurangi resiko kerugian. Tujuan dari penelitian ini dimaksudkan memprediksi bisnis fores menggunakan model neural network dengan data time series per 1 menit untuk mengetahui nilai akurasi prediksi sehingga dapat mengurangi resiko dalam menjalankan bisnis forex. Metode penelitian pada penelitian ini meliputi metode pengumpulan data kemudian dilanjutkan ke metode training, learning, testing menggunakan neural network. Setelah di evaluasi hasil penelitian ini menunjukan bahwa penerapan algoritma Neural Network mampu untuk memprediksi forex dengan tingkat akurasi prediksi 0.431 +/-0.096 sehingga dengan prediksi ini dapat membantu mengurangi resiko dalam menjalankan bisnis forex. Kata kunci: prediksi, forex, neural network. ABSTRACT Prediction is one of the most important techniques in running forex business. Decision to predict is very important, because the predictions can help determine the value of forex at a certain time in the

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“…Generally, tabular patterns can have more dimensions. Graphical representation of OHLC data (Lai, 2005) in Fig.2 is a good example of the expression of multidimensional data projection to two-dimensional space. Fig.2 shows a visual representation of 4-dimensional vector in time, which corresponds to 5 -dimensions.…”

Section: Fig 1 Tasks In the Pattern Recognition Systemsmentioning

confidence: 99%