Evaluation of bidirectional LSTM for short-and long-term stock market prediction

Al-Thelaya, Khaled A.; El-Alfy, El-Sayed M.; Mohammed, Salahadin

doi:10.1109/iacs.2018.8355458

Cited by 181 publications

(73 citation statements)

References 15 publications

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“…(c) We have also experimented with Bidirectional LSTM (Bi-LSTM). Compared to the LSTM, the Bi-LSTM has used two layers; one layer performs the operations following the forward direction (time-series data) of the data sequence, and the other layer applies its operations on in the reverse direction of the data sequence [81].…”

Section: Selection Of An Appropriate Techniquementioning

confidence: 99%

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Deep learning-based effective fine-grained weather forecasting model

Hewage

Trovati

Pereira

et al. 2020

Pattern Anal Applic

164

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It is well-known that numerical weather prediction (NWP) models require considerable computer power to solve complex mathematical equations to obtain a forecast based on current weather conditions. In this article, we propose a novel lightweight data-driven weather forecasting model by exploring temporal modelling approaches of long short-term memory (LSTM) and temporal convolutional networks (TCN) and compare its performance with the existing classical machine learning approaches, statistical forecasting approaches, and a dynamic ensemble method, as well as the well-established weather research and forecasting (WRF) NWP model. More specifically Standard Regression (SR), Support Vector Regression (SVR), and Random Forest (RF) are implemented as the classical machine learning approaches, and Autoregressive Integrated Moving Average (ARIMA), Vector Auto Regression (VAR), and Vector Error Correction Model (VECM) are implemented as the statistical forecasting approaches. Furthermore, Arbitrage of Forecasting Expert (AFE) is implemented as the dynamic ensemble method in this article. Weather information is captured by time-series data and thus, we explore the state-of-art LSTM and TCN models, which is a specialised form of neural network for weather prediction. The proposed deep model consists of a number of layers that use surface weather parameters over a given period of time for weather forecasting. The proposed deep learning networks with LSTM and TCN layers are assessed in two different regressions, namely multi-input multi-output and multi-input single-output. Our experiment shows that the proposed lightweight model produces better results compared to the well-known and complex WRF model, demonstrating its potential for efficient and accurate weather forecasting up to 12 h.

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Section: Selection Of An Appropriate Techniquementioning

confidence: 99%

“…The Bi-LSTM is also trained the model at the beginning similar to the LSTM WL. However, the Bi-LSTM provides more accurate results due to the ability to preserve the past and future values [81].…”

Section: Selection Of An Appropriate Techniquementioning

confidence: 99%

Deep learning-based effective fine-grained weather forecasting model

Hewage

Trovati

Pereira

et al. 2020

Pattern Anal Applic

164

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“…LSTM [34], as an enhanced recurrent neural network unit, is good at processing sequential information that has long-term dependencies, such as text sequences. Rather than processing the information based on only one direction, a Bi-LSTM [35][36][37] is able to process the sequences from both directions. A convolutional neural network (CNN) is commonly used in tandem with a LSTM in order to reduce the length of sequences, which can significantly facilitate the speed of training [38][39][40].…”

Section: Baseline Deep Learning Modelsmentioning

confidence: 99%

Toward multi-label sentiment analysis: a transfer learning based approach

2020

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Sentiment analysis is recognized as one of the most important sub-areas in Natural Language Processing (NLP) research, where understanding implicit or explicit sentiments expressed in social media contents is valuable to customers, business owners, and other stakeholders. Researchers have recognized that the generic sentiments extracted from the textual contents are inadequate, thus, Aspect Based Sentiment Analysis (ABSA) was coined to capture aspect sentiments expressed toward specific review aspects. Existing ABSA methods not only treat the analytical problem as single-label classification that requires a fairly large amount of labelled data for model training purposes, but also underestimate the entity aspects that are independent of certain sentiments. In this study, we propose a transfer learning based approach tackling the aforementioned shortcomings of existing ABSA methods. Firstly, the proposed approach extends the ABSA methods with multi-label classification capabilities. Secondly, we propose an advanced sentiment analysis method, namely Aspect Enhanced Sentiment Analysis (AESA) to classify text into sentiment classes with consideration of the entity aspects. Thirdly, we extend two state-of-the-art transfer learning models as the analytical vehicles of multi-label ABSA and AESA tasks. We design an experiment that includes data from different domains to extensively evaluate the proposed approach. The empirical results undoubtedly exhibit that the proposed approach outperform all the baseline approaches.

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“…Considering that the stacked architecture can enforce a deeper analysis on the training data to model more sophisticated data patterns, and bidirectional LSTMs have the ability to process and learn from data in the past and future time directions and combine forward and backward contextual information, Ref. [26] exploited the merits of both architectures, and similar to [25], for both short-term and long-term predictions of financial time series, bidirectional LSTM networks achieved better performance and convergence.…”

Section: Deep Learning-based Prediction Modelsmentioning

confidence: 99%

BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market

Chen

Wang

et al. 2019

Energies

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For the benefit from accurate electricity price forecasting, not only can various electricity market stakeholders make proper decisions to gain profit in a competitive environment, but also power system stability can be improved. Nevertheless, because of the high volatility and uncertainty, it is an essential challenge to accurately forecast the electricity price. Considering that recurrent neural networks (RNNs) are suitable for processing time series data, in this paper, we propose a bidirectional long short-term memory (LSTM)-based forecasting model, BRIM, which splits the state neurons of a regular RNN into two parts: the forward states (using the historical electricity price information) are designed for processing the data in positive time direction and backward states (using the future price information available at inter-connected markets) for the data in negative time direction. Moreover, due to the fact that inter-connected power exchange markets show a common trend for other neighboring markets and can provide signaling information for each other, it is sensible to incorporate and exploit the impact of the neighboring markets on forecasting accuracy of electricity price. Specifically, future electricity prices of the interconnected market are utilized both as input features for forward LSTM and backward LSTM. By testing on day-ahead electricity prices in the European Power Exchange (EPEX), the experimental results show the superiority of the proposed method BRIM in enhancing predictive accuracy in comparison with the various benchmarks, and moreover Diebold-Mariano (DM) shows that the forecast accuracy of BRIM is not equal to other forecasting models, and thus indirectly demonstrates that BRIM statistically significantly outperforms other schemes.

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Evaluation of bidirectional LSTM for short-and long-term stock market prediction

Cited by 181 publications

References 15 publications

Deep learning-based effective fine-grained weather forecasting model

Deep learning-based effective fine-grained weather forecasting model

Toward multi-label sentiment analysis: a transfer learning based approach

BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market

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