A CNN–LSTM model for gold price time-series forecasting

Livieris, Ioannis E.; Pintelas, Emmanuel G.; Pintelas, Panayiotis E.

doi:10.1007/s00521-020-04867-x

Cited by 565 publications

(253 citation statements)

References 25 publications

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“…Theoretical explanations of benchmark models, LSTM [31], CNN-LSTM [32] and SVR [33] (Support Vector Regression), are available in studies elsewhere.…”

Section: B Theoretical Overviewmentioning

confidence: 99%

Designing Deep-Based Learning Flood Forecast Model With ConvLSTM Hybrid Algorithm

et al. 2021

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Efficient, robust, and accurate early flood warning is a pivotal decision support tool that can help save lives and protect the infrastructure in natural disasters. This research builds a hybrid deep learning (ConvLSTM) algorithm integrating the predictive merits of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to design and evaluate a flood forecasting model to forecast the future occurrence of flood events. Derived from precipitation dataset, the work adopts a Flood Index (), in form of a mathematical representation, to capture the gradual depletion of water resources over time, employed in a flood monitoring system to determine the duration, severity, and intensity of any flood situation. The newly designed predictive model utilizes statistically significant lagged , improved by antecedent and real-time rainfall data to forecast the next daily value. The performance of the proposed ConvLSTM model is validated against 9 different rainfall datasets in flood prone regions in Fiji which faces flood-driven devastations almost annually. The results illustrate the superiority of ConvLSTM-based flood model over the benchmark methods, all of which were tested at the 1-day, 3-day, 7-day, and the 14-day forecast horizon. For instance, the Root Mean Squared Error (RMSE) for the study sites were 0.101, 0.150, 0.211 and 0.279 for the four forecasted periods, respectively, using ConvLSTM model. For the next best model, the RMSE values were 0.105, 0.154, 0.213 and 0.282 in that same order for the four forecast horizons. In terms of the difference in model performance for individual stations, the Legate-McCabe Efficiency Index (LME) were 0.939, 0.898, 0.832 and 0.726 for the four forecast horizons, respectively. The results demonstrated practical utility of ConvLSTM in accurately forecasting and its potential use in disaster management and risk mitigation in the current phase of extreme weather events.

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“…Theoretical explanations of benchmark models, LSTM [31], CNN-LSTM [32] and SVR [33] (Support Vector Regression), are available in studies elsewhere.…”

Section: B Theoretical Overviewmentioning

confidence: 99%

Designing Deep-Based Learning Flood Forecast Model With ConvLSTM Hybrid Algorithm

et al. 2021

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“…The financial data of various instruments are forecasted using artificial intelligence on the basis of Long Short-term Memory (LSTM) (Cao ey al. 2019, Bukhari et al 2020, Livieris et al 2020, Gated Recurrent Unit (GRU) , Munkhdalai et al 2020, and the comparison of the forecasting accuracy of both these instruments with the popular ARMA is presented by Yamak et al 2019. The application of computational intelligence in the derivatives market is also interesting for researchers.…”

Section: Literature Reviewmentioning

confidence: 99%

Trading support method based on computational intelligence for speculators in the options market

Maknickienė¹,

Maknickas²,

Martinkutė-Kaulienė³

2020

Journal of International Studies

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The financial world has changed dramatically in recent decades. Electronic data processing, globalisation, and deregulation have changed markets, and the biggest part of these major changes includes derivatives. As financial markets become more interconnected and global, volatility in these markets may increase dramatically in the future. It is natural that the derivatives market is gaining attention and popularity among market participants as an alternative to traditional investment and speculative instruments. The growing number of technologydriven applications and innovations in the financial sector encourages the inclusion of products relating to automated trading and robotic advice in financial decision-making. The aim of this paper is to investigate different option-trading strategies and to evaluate the effect of computational intelligence on trading success in the derivatives markets. The recurrent neural network (RNN) Keras was adopted for forecasting option prices, and the results were compared with the forecasting using the evolution of recurrent systems with optimal linear output algorithms (EVOLINO) for RNNs. This forecasting

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“…These hybrid models have performed well in CTSP. More recently, deep learning algorithms such as long short-term memory neural network (LSTM) [18], convolutional neural network (CNN) [19], and hybrid CNN-LSTM neural network have been applied to CTSP [20]- [22]. YanLi et al applied hybrid empirical mode decomposition, adaptive regrouped, and LSTM to forecast port cargo throughput time series [23].…”

Section: Introductionmentioning

confidence: 99%

“…YanLi et al applied hybrid empirical mode decomposition, adaptive regrouped, and LSTM to forecast port cargo throughput time series [23]. Compared to the hybrid machine learning model, the hybrid deep learning model has a better performance [22]. In the last few years, a particular hybrid model named neuro-evolution has once again caught the attention of researchers.…”

Section: Introductionmentioning

confidence: 99%

Deep Hybrid Neural Network and Improved Differential Neuroevolution for Chaotic Time Series Prediction

Huang

Yuan

2020

IEEE Access

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Chaos is widespread in non-linear systems such as finance, energy, and weather. In the chaos system, a variable changing with time generates a chaotic time series, which contains a wealth of information about the non-linear system, and it is helpful for us to analyze and understand chaos systems. Traditional hybrid models for chaotic time series prediction based on neural networks have problems such as low prediction accuracy and difficulty in determining the network topologies. In recent years, the chaotic time series prediction has attached the attention of researchers in the area of deep learning. In this paper, we use a deep hybrid neural network (DHNN) based on convolutional neural network (CNN), gated recurrent unit (GRU) network, and attention mechanism to predict chaotic time series. Besides, we use the idea of neuroevolution to optimize the topologies of the DHNN. In the DHNN, we use CNN to capture spatial features from phase space reconstruction of chaotic time series. Then, we combine spatial features with the original chaotic time series. GRU extracts the spatio-temporal features from the combined sequence, and an attention mechanism with a non-linear activation function is designed to capture critical spatio-temporal features. Besides, we propose an improved differential evolution (IDE) algorithm to optimize the topologies of the DHNN, including the filter sizes of CNN and the number of hidden neurons of GRU. We develop the IDE with an adaptive mutation operator and dynamic chaos crossover operator, which can improve convergence speed and reduce optimization time. In this paper, we use the theoretical Lorenz datasets, monthly mean total sunspot datasets, and the actual coalmine gas concentration datasets to verify the prediction accuracy of the proposed prediction model. Experimental results have shown that the proposed prediction model performs well in chaotic time series forecasting. INDEX TERMS Chaotic time series prediction, convolutional neural network, gated recurrent unit, attention mechanism, improved differential evolution, neuroevolution

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A CNN–LSTM model for gold price time-series forecasting

Cited by 565 publications

References 25 publications

Designing Deep-Based Learning Flood Forecast Model With ConvLSTM Hybrid Algorithm

Designing Deep-Based Learning Flood Forecast Model With ConvLSTM Hybrid Algorithm

Trading support method based on computational intelligence for speculators in the options market

Deep Hybrid Neural Network and Improved Differential Neuroevolution for Chaotic Time Series Prediction

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