Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Yang, Can; Zhai, Junjie; Tao, Guihua

doi:10.1155/2020/2746845

Cited by 33 publications

(16 citation statements)

References 37 publications

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“…The approach was robust regarding stock price prediction; however, the prediction accuracy requires enhancements. Similarly, LSTM based approach was presented in [26] to give the prediction of stock prices using correlated STIs. This work improved the stock movement prediction performance; however, this was at the expense of increased computational cost.…”

Section: Related Workmentioning

confidence: 99%

AEI-DNET: A Novel DenseNet Model with an Autoencoder for the Stock Market Predictions Using Stock Technical Indicators

et al. 2022

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Predicting stock market prices is an important and interesting task in academic and financial research. The volatile nature of the stock market means that predicting stock market prices is a challenging task. However, recent advancements in machine learning, especially in deep learning techniques, have made it possible for researchers to use such techniques to predict future stock trends based on historical financial data, social media news, financial news, and stock technical indicators (STIs). This work focused on the prediction of closing stock prices based on using ten years of Yahoo Finance data of ten renowned stocks and STIs by using 1D DenseNet and an autoencoder. The calculated STIs were first used as the input for the autoencoder for dimensionality reduction, resulting in less correlation between the STIs. These STIs, along with the Yahoo finance data, were then fed into the 1D DenseNet. The resultant features obtained from the 1D DenseNet were then used as input for the softmax layer residing inside the 1D DenseNet framework for the prediction of closing stock prices for short-, medium-, and long-term perspectives. Based on the predicted trends of the stock prices, our model presented the user with one of three suggested signals, i.e., buy, sell, or hold. The experimental results showed that the proposed approach outperformed the state-of-the-art techniques by obtaining a minimum MAPE value of 0.41.

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Section: Related Workmentioning

confidence: 99%

AEI-DNET: A Novel DenseNet Model with an Autoencoder for the Stock Market Predictions Using Stock Technical Indicators

et al. 2022

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“…Dalam beberapa tahun terakhir, penerapan arsitektur pembelajaran mendalam seperti Long-Short Term Memory (LSTM) (Shams et al, 2020), Recurrent Neural Network (RNN) (Hewamalage et al, 2021), Convolutional Neural Network (CNN) (Yang et al, 2020) dan lainnya dilaporkan menghasilkan tingkat akurasi yang menjanjikan untuk peramalan deret waktu. RNN adalah salah satu metode pembelajaran mendalam yang digunakan untuk menemukan korelasi temporal dalam prediksi deret waktu (Sherstinsky, 2020) berkinerja baik terhadap informasi terbaru, tetapi sulit untuk memodelkan ketergantungan jangka panjang (Sherstinsky, 2020).…”

Section: Pendahuluanunclassified

Model Prediksi Data Besar Distribusi Produk Farmasi: Analisis Kinerja Model Deep Learning

Fadli¹,

Suwilo²,

Wang³

2022

CSRID

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Seiring dengan berjalannya bisnis perusahaan, masalah dalam penyimpanan dan pengolahan data besar pun akan semakin kompleks. data yang tidak terorganisir dapat menyebabkan perusahaan gagal dalam memaksimalkan strategi penjualan. Salah satu pendekatan untuk memaksimalkan strategi penjualan tersebut adalah dengan peramalan.penelitian ini bertujuan untuk mengurangi tingkat persediaan pelanggan jangka pendek dan membantu dalam menentukan target penjualan yang realistis di masa depan dengan mengusulkan metode pembelajaran mendalam berdasarkan segmentasi pelanggan. Kerangka analisa diusulkan menggunakan teknik Robust Principal Component Analysis (RPCA) untuk mengurangi dimensi kumpulan dataset, kemudian algoritma K-Means Clustering diterapkan untuk mengidentifikasi kelompok populasi guna melihat beberapa kluster yang dapat sangat mewakili karakteristik basis pelanggan perusahaan yang ada. Terakhir lapisan CNN dan LSTM digabungkan untuk memperkirakan penjualan masa depan. Hasil peramalan dievaluasi menggunakan Mean Absolute Error (MAE) dan Root Mean Square Error (RMSE). Pendekatan yang diusulkan guna mengisi celah masalah yang terjadi karena kurangnya informasi mengenai kurangnya informasi tentang kinerja bisnis dalam hal kategorisasi produk.

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“…There are still some problems with pure causal convolution, such as difficulty in capturing the dependencies between longer interval time points. Dilated convolution allows spaced sampling of the convolution time point input, and the sampling rate is controlled by the parameter Dilate [13]. The higher the level, the larger the Dilate used.…”

Section: Dilated Convolutionmentioning

confidence: 99%

Stock Price Prediction Model based on Dual Attention and TCN

Fu¹,

He²

2022

Signal, Image Processing and Embedded Systems Trends

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The stock market is affected by many variables and factors, and the current forecasting models for time series are often difficult to capture the complex laws among multiple factors. Aiming at this problem, a stock price prediction model based on dual attention mechanism and temporal convolutional network is proposed. First, a convolution network more suitable for time series is used as the feature extraction layer. Feature attention is introduced to dynamically mine the potential correlation between the input factor features and closing prices. Second, based on Gated Recurrent Unit, on the other hand, a temporal attention mechanism is introduced to improve the model's ability to learn important time points and obtain importance measures from a temporal perspective. The experimental results show that the proposed model performs better than the traditional prediction model in the error index of stock price prediction and realizes the interpretability of the model in terms of index characteristics and time.

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Deep Learning for Price Movement Prediction Using Convolutional Neural Network and Long Short-Term Memory

Cited by 33 publications

References 37 publications

AEI-DNET: A Novel DenseNet Model with an Autoencoder for the Stock Market Predictions Using Stock Technical Indicators

AEI-DNET: A Novel DenseNet Model with an Autoencoder for the Stock Market Predictions Using Stock Technical Indicators

Model Prediksi Data Besar Distribusi Produk Farmasi: Analisis Kinerja Model Deep Learning

Stock Price Prediction Model based on Dual Attention and TCN

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