Improving Financial Time Series Prediction Accuracy Using Ensemble Empirical Mode Decomposition and Recurrent Neural Networks

Chacón, Henry; Kesici, Emre; Rad, Paul

doi:10.1109/access.2020.2996981

Cited by 33 publications

(19 citation statements)

References 37 publications

(67 reference statements)

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“…LSTM and Seq2Seq methods are easily adapted to both univariate or multivariate time series prediction (Rebane et al., 2018; Torres & Qiu, 2018). It should be noted that some authors have identified weaknesses and limitations in using machine learning methods in general and LSTM methods in particular for time series forecasting (e.g., Chacón et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Time‐Series Prediction Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data

et al. 2021

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Time series of displacement are now routinely available from satellite InSAR and are used for flagging anomalous ground motion, but not yet forecasting. We test conventional time series forecasting methods such as SARIMA and supervised machine learning approaches such as long short‐term memory (LSTM) compared to simple function extrapolation. We focus initially on forecasting seasonal signals and begin by characterizing the time‐series using sinusoid fitting, seasonal decomposition, and autocorrelation functions. We find that the three measures are broadly comparable but identify different types of seasonal characteristic. We use this to select a set of 310 points with highly seasonal characteristics and test the three chosen forecasting methods over prediction windows of 1–9 months. The lowest overall median RMSE values are obtained for SARIMA when considering short term predictions (<1 month), whereas sinusoid extrapolation produces the lowest median RMSE values for longer predictions (>6 months). Machine learning methods (LSTM) perform less well. We then test the prediction methods on 2,000 randomly selected points with a range of seasonalities and find that simple extrapolation of a constant function performed better overall than any of the more sophisticated time series prediction methods. Comparisons between seasonality and RMSE show a small improvement in performance with increasing seasonality. This proof‐of‐concept study demonstrates the potential of time‐series prediction for InSAR data but also highlights the limitations of applying these techniques to nonperiodic signals or individual measurement points. We anticipate future developments, especially to shorter timescales, will have a broad range of potential applications, from infrastructure stability to volcanic eruptions.

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Section: Introductionmentioning

confidence: 99%

Time‐Series Prediction Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data

et al. 2021

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“…The second most significant journal is IEEE Access, this journal maintains a special section highlighting specific topics of IEEE interest. IEEE Access is published by the Institute of Electrical and Electronics Engineers (IEEE) with a concentration area with specialization in the application of science, technology, engineering, and mathematics [26]. There is no doubt that the quality of the IEEE Acess journal has a score of 0.78 in the Q1 category in Computer Science.…”

Section: Significant Published Journalmentioning

confidence: 99%

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Wiranata

Djunaidy

2021

Jurnal Ilmu Komputer dan Informasi

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This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization.

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“…Firstly, in the existing research on forecasting models for natural gas prices, the decomposition of the original series by VMD technology usually only uses onelevel decomposition technology; besides, there is no published research on forecasting natural gas prices by combining secondary decomposition technology with a deep learning forecasting model. In addition, the research on prediction models combining decomposition technology and LSTM models ignores the residual term after decomposition, failing to consider the complex information contained in the residual after modal decomposition and weakening the decomposition efect of data [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Natural Gas Price Forecasting by a New Hybrid Model Combining Quadratic Decomposition Technology and LSTM Model

Zhan

Tang

2022

Mathematical Problems in Engineering

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Research on the price prediction of natural gas is of great significance to market participants of all kinds. In order to predict natural gas prices more reliably, this paper introduces a quadratic decomposition technology based on the combination of variational modal decomposition (VMD) and ensemble empirical modal decomposition (EEMD), which decomposes the residual term (Res) after VMD by EEMD; then, a new hybrid model called VMD-EEMD-Res.-LSTM is constructed in combination with the long short-term memory (LSTM) prediction model. The contribution of this new hybrid model is that, unlike existing application research that combines existing decomposition technology with the LSTM model, it does not ignore the important information contained in the residual after the VMD. In order to verify the predictive performance of the proposed new model, this paper uses the data of the spot price of natural gas in the United States to conduct a multistep-ahead empirical comparative analysis. The results show that the new hybrid model constructed in this paper has significant predictive advantages.

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Improving Financial Time Series Prediction Accuracy Using Ensemble Empirical Mode Decomposition and Recurrent Neural Networks

Cited by 33 publications

References 37 publications

Time‐Series Prediction Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data

Time‐Series Prediction Approaches to Forecasting Deformation in Sentinel‐1 InSAR Data

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

Natural Gas Price Forecasting by a New Hybrid Model Combining Quadratic Decomposition Technology and LSTM Model

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