A Review of Long Short-Term Memory Approach for Time Series Analysis and Forecasting

Kader, Nur Izzati Ab; Yusof, Umi Kalsom; Khalid, Mohd Nor Akmal; Husain, Nik Rosmawati Nik

doi:10.1007/978-3-031-20429-6_2

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…To address these limitations, the LSTM algorithm can be used, as it can capture both non-linearity and sequential information in the data. The LSTM is a powerful time series model that is derived from the recurrent neural network (RNN) structure [11,12]. Unlike RNNs, LSTM can memorize long-term sequential information by using extra memory lines and control gates, making it a suitable algorithm for time series problems such as stock prediction [13].…”

Section: Related Workmentioning

confidence: 99%

Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market

Ku,

Xiong,

Chen

et al. 2023

Mathematics

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Stock market predictions are a challenging problem due to the dynamic and complex nature of financial data. This study proposes an approach that integrates the domain knowledge of investors with a long-short-term memory (LSTM) algorithm for predicting stock prices. The proposed approach involves collecting data from investors in the form of technical indicators and using them as input for the LSTM model. The model is then trained and tested using a dataset of 100 stocks. The accuracy of the model is evaluated using various metrics, including the average prediction accuracy, average cumulative return, Sharpe ratio, and maximum drawdown. The results are compared to the performance of other strategies, including the random selection of technical indicators. The simulation results demonstrate that the proposed model outperforms the other strategies in terms of accuracy and performance in a 100-stock investment simulation, highlighting the potential of integrating investor domain knowledge with machine learning algorithms for stock price prediction.

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Section: Related Workmentioning

confidence: 99%

Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market

Ku,

Xiong,

Chen

et al. 2023

Mathematics

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“…The primary contrast between recurrent neural networks (RNNs) and LSTMs is that LSTMs are an advancement of RNNs that enhances their memory. LSTMs exhibit superior performance in retaining long-term dependencies in the data compared to RNNs, accomplished by utilizing special memory cells and gate functions [6].…”

Section: Introductionmentioning

confidence: 99%

Prediction of US 30-years-treasury-bonds mouvement and trading entry point using Robust 1DCNN-BiLSTM-XGBoost algorithm

Zaar

Benaya

Bakir

et al. 2023

Preprint

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This paper proposes a novel algorithm that accurately predicts market trends and trading entry points for US 30-year-treasury bonds using a hybrid approach of 1-Dimensional Convolutional Neural Network (1DCNN), Long-Short Term Memory (LSTM), and XGBoost algorithms. We compared the performance of various strategies using 1DCNN and LSTM and found that existing state-of-the-art methods based on LSTM have excellent results in market movement prediction tasks, but the effectiveness of 1DCNN and LSTM in terms of trading entry point and market perturbations has not been studied thoroughly. We demonstrate, through experiments that our proposed 1DCNN-BiLSTM-XGBoost algorithm combined with moving averages crossover effectively mitigates noise and market perturbations, leading to high accuracy in spotting trading entry points and trend signals for US 30-year-treasury-bonds. Our experimental study shows that the proposed approach achieves an average of 0.0001% Root Mean Squared Error and 100% R-Square, making it a promising method for predicting the market trends and trading entry points.

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mentioning

confidence: 99%

Prediction of US 30‐years‐treasury‐bonds movement and trading entry point using the robust 1DCNN‐BiLSTM‐XGBoost algorithm

El Zaar,

Benaya,

Bakir

et al. 2023

Expert Systems

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This article presents a novel algorithm that accurately predicts market trends and identifies trading entry points for US 30‐year Treasury bonds. The proposed method employs a hybrid approach, integrating a 1‐dimensional convolutional neural network (1DCNN), long‐short term memory (LSTM), and XGBoost algorithms. The 1DCNN is used to learn local and short‐term patterns, while LSTM is employed to capture both short and long‐term dependencies. Furthermore, we have implemented an algorithm that utilizes hull moving average (HMA) and simple moving average (SMA) crossover data to detect trading entry points and major trends in the market. The combination of the SMA–HMA crossover algorithm and predictions provided by the 1DCNN‐BiLSTM‐XGBoost algorithm yields exceptional results in terms of prediction accuracy and profitability. Additionally, these integrated techniques effectively filter out noise and mitigate false breakouts, which are often observed with US 30‐year Treasury bonds. In the field of financial time series prediction, the effectiveness of 1DCNN and LSTM in identifying trading entry points and market perturbations has not been comprehensively studied. Therefore, our work fills this gap by demonstrating through experiments that the proposed 1DCNN‐BiLSTM‐XGBoost algorithm, in combination with moving average crossovers, effectively reduces noise and market perturbations. This leads to the precise identification of trading entry points and accurate recognition of trend signals for US 30‐year Treasury bonds. We demonstrate through experiments that our proposed approach achieves an average root mean squared error of 0.0001 and an R‐square value of 0.9999, highlighting its promise as a method for predicting market trends and trading entry points for US 30‐year Treasury bonds.

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A Review of Long Short-Term Memory Approach for Time Series Analysis and Forecasting

Cited by 6 publications

References 35 publications

Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market

Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market

Prediction of US 30-years-treasury-bonds mouvement and trading entry point using Robust 1DCNN-BiLSTM-XGBoost algorithm

Prediction of US 30‐years‐treasury‐bonds movement and trading entry point using the robust 1DCNN‐BiLSTM‐XGBoost algorithm

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