Time-Weighted LSTM Model with Redefined Labeling for Stock Trend Prediction

Zhao, Zhiyong; Rao, Ruonan; Tu, S; Shi, Jun

doi:10.1109/ictai.2017.00184

Cited by 51 publications

(30 citation statements)

References 7 publications

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“…As deep learning [26]- [28] and artificial intelligence [29], [30] have achieved great success in computer vision, they are also applied in BDA for predicting trends and classification. In Zhao et al [31] and Dai et al [32], Long Short-Term Memory networks were successfully applied to predict stock price and gas dissolved in power transformation. In Kashef et al [33], a neural network was applied on a smart grid system to estimate the trends of power loss.…”

Section: B Crime Data Mining Visualization and Trends Forecastingmentioning

confidence: 99%

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

et al. 2019

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Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for visualization and trends prediction. Several the state-of-theart data mining and deep learning techniques are used. Following statistical analysis and visualization, some interesting facts and patterns are discovered from criminal data in San Francisco, Chicago, and Philadelphia. The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, where the optimal size of the training data is found to be three years. These promising outcomes will benefit for police departments and law enforcement organizations to better understand crime issues and provide insights that will enable them to track activities, predict the likelihood of incidents, effectively deploy resources and optimize the decision making process. INDEX TERMS Big data analytics (BDA), data mining, data visualization, neural network, time series forecasting.

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Section: B Crime Data Mining Visualization and Trends Forecastingmentioning

confidence: 99%

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

et al. 2019

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“…Learning Representations across days Analyzing a temporal sequence of tweets and combining them can provide a more reliable assessment of market trends (Zhao et al, 2017). We learn a social media representation from the sequence of day level tweet representations r i .…”

Section: Social Media Information Encoder (Smi)mentioning

confidence: 99%

Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations

Sawhney

Agarwal²,

Wadhwa

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task. Stock forecasting is complex, given the stochastic dynamics and non-stationary behavior of the market. Stock movements are influenced by varied factors beyond the conventionally studied historical prices, such as social media and correlations among stocks. The rising ubiquity of online content and knowledge mandates an exploration of models that factor in such multimodal signals for accurate stock forecasting. We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion. Through experiments on real-world S&P 500 index data and English tweets, we show the practical applicability of our model as a tool for investment decision making and trading.

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“…As such, through the gate structure feature, the information was retained and persistently updated in the following training iterations. For instance, Zhao et al used LSTM to achieve extraordinary performance in stock trend prediction [10]. Seng et al used the ordinary three-layer LSTM structure, instead of utilizing too complex network structures, to forecast LQ45 financial sectors indices and obtain nice results [11].…”

Section: Related Workmentioning

confidence: 99%

Forecasting Variation Trends of Stocks via Multiscale Feature Fusion and Long Short-Term Memory Learning

Liu

et al. 2021

Scientific Programming

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Forecasting stock price trends accurately appears a huge challenge because the environment of stock markets is extremely stochastic and complicated. This challenge persistently motivates us to seek reliable pathways to guide stock trading. While the Long Short-Term Memory (LSTM) network has the dedicated gate structure quite suitable for the prediction based on contextual features, we propose a novel LSTM-based model. Also, we devise a multiscale convolutional feature fusion mechanism for the model to extensively exploit the contextual relationships hidden in consecutive time steps. The significance of our designed scheme is twofold. (1) Benefiting from the gate structure designed for both long- and short-term memories, our model can use the given stock history data more adaptively than traditional models, which greatly guarantees the prediction performance in financial time series (FTS) scenarios and thus profits the prediction of stock trends. (2) The multiscale convolutional feature fusion mechanism can diversify the feature representation and more extensively capture the FTS feature essence than traditional models, which fairly facilitates the generalizability. Empirical studies conducted on three classic stock history data sets, i.e., S&P 500, DJIA, and VIX, demonstrated the effectiveness and stability superiority of the suggested method against a few state-of-the-art models using multiple validity indices. For example, our method achieved the highest average directional accuracy (around 0.71) on the three employed stock data sets.

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Time-Weighted LSTM Model with Redefined Labeling for Stock Trend Prediction

Cited by 51 publications

References 7 publications

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations

Forecasting Variation Trends of Stocks via Multiscale Feature Fusion and Long Short-Term Memory Learning

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