Gold Price Forecast Based on LSTM-CNN Model

He, Zhanhong; Zhou, Junhao; Dai, Hong-Ning; Wang, Hao

doi:10.1109/dasc/picom/cbdcom/cyberscitech.2019.00188

Cited by 40 publications

(22 citation statements)

References 11 publications

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“…In studies conducted by Siami-Namini et al (2018), LSTM-based algorithms show significant improvement in the accuracy of future prediction as compared to ARIMA. He et al, (2019) supported this in their finding where LSTM-based models performed significantly better than ARIMA in forecasting precious metal prices. Wu et al, (2018) proposed LSTM forecasting framework in the forecast of Bitcoin volatility concluded that values projected by LSTM are nearer to the actual values on a non-stationary time series data.…”

Section: Introductionmentioning

confidence: 71%

“…While the forecasting of gold bullion price is a well-researched area (Alameer et al, 2019;Hadavandi et al, 2010;He et al, 2019;Livieris et al, 2020;Pandey et al, 2019;Ping et al, 2013;Vidal & Kristjanpoller, 2020;Zhang & Liao, 2014), studies that specifically predicts the Kijang Emas gold coin price remains limited. Miswan et al, (2013) used the Box-Jenkins methodology to develop an ARIMA-based model to predict the Kijang Emas value while Ping et al, (2013) produced a GARCH model which can provide a better prediction than the ARIMA-based models.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Kijang Emas Bullion Price using LSTM Networks

Ismail¹,

Razak²

2020

J ENTREP BUS

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This study investigates the potential of Deep Learning techniques, specifically LSTM networks, in forecasting Kijang Emas future value over a long period. Six LSTM models comprising of Simple LSTM, Bidirectional LSTM, and Stacked LSTM architecture were built and trained against a 15-year historical price data for Kijang Emas. The models’ performance was then measured against ARIMA (5,1,0) as a baseline reference and evaluated against the RAE, MSE and RMSE metric. The results revealed that LSTM networks models performed well in forecasting Kijang Emas price based on the test dataset where the average RMSE was between 49.9 to 50.3 while the Bidirectional LSTM was found to exhibit better performance as compared to the other LSTM models.

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Section: Introductionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Predicting the Kijang Emas Bullion Price using LSTM Networks

Ismail¹,

Razak²

2020

J ENTREP BUS

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“…ARIMA, RF, and XGBoost were executed without any further configurations. [15] This approach first uses the LSTM model, followed by CNN for parameter classification. Initially, this model was used to predict gold prices.…”

Section: Baselinesmentioning

confidence: 99%

Civil airline fare prediction with a multi-attribute dual-stage attention mechanism

et al. 2021

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Airfare price prediction is one of the core facilities of the decision support system in civil aviation, which includes departure time, days of purchase in advance and flight airline. The traditional airfare price prediction system is limited by the nonlinear interrelationship of multiple factors and fails to deal with the impact of different time steps, resulting in low prediction accuracy. To address these challenges, this paper proposes a novel civil airline fare prediction system with a Multi-Attribute Dual-stage Attention (MADA) mechanism integrating different types of data extracted from the same dimension. In this method, the Seq2Seq model is used to add attention mechanisms to both the encoder and the decoder. The encoder attention mechanism extracts multi-attribute data from time series, which are optimized and filtered by the temporal attention mechanism in the decoder to capture the complex time dependence of the ticket price sequence. Extensive experiments with actual civil aviation data sets were performed, and the results suggested that MADA outperforms airfare prediction models based on the Auto-Regressive Integrated Moving Average (ARIMA), random forest, or deep learning models in MSE, RMSE, and MAE indicators. And from the results of a large amount of experimental data, it is proven that the prediction results of the MADA model proposed in this paper on different routes are at least 2.3% better than the other compared models.

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“…It should be noted that CNN methods can handle inherent data complexities, e.g., spatio-temporality, chaotic nature, and non-stationarity, only in cases when they are designed and/or evaluated for each specific dataset and application. To let CNNs work, they need to be initialized with a large number of labels [47].…”

Section: Compressed Sensing-convolutional Neural Network: the Proposmentioning

confidence: 99%

Hybrid Convolutional Neural Networks Based Framework for Skimmed Milk Powder Price Forecasting

Malczewski

Czubak

2021

Sustainability

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The latest studies have compellingly argued that Neural Networks (NN) classification and prediction are the right direction for forecasting. It has been proven that NN are suitable models for any continuous function. Moreover, these methods are superior to conventional methods, such a Box–Jenkins, AR, MA, ARMA, or ARIMA. The latter assume a linear relationship between inputs and outputs. This assumption is not valid for skimmed milk powder (SMP) forecasting, because of nonlinearities, which are supposed to be approximated. The traditional prediction methods need complete date. The non-AI-based techniques regularly handle univariate-like data only. This assumption is not sufficient, because many external factors might influence the time series. It should be noted that any Artificial Neural Network (ANN) approach can be strongly affected by the relevancy and “clarity” of its input training data. In the proposed Convolutional Neural Networks based methodology assumes price series data to be sparse and noisy. The presented procedure utilizes Compressed Sensing (CS) methodology, which assumes noisy trends are incomplete signals for them to be reconstructed using CS reconstruction algorithms. Denoised trends are more relevant in terms of NN-based forecasting models’ prediction performance. Empirical results reveal robustness of the proposed technique.

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Gold Price Forecast Based on LSTM-CNN Model

Cited by 40 publications

References 11 publications

Predicting the Kijang Emas Bullion Price using LSTM Networks

Predicting the Kijang Emas Bullion Price using LSTM Networks

Civil airline fare prediction with a multi-attribute dual-stage attention mechanism

Hybrid Convolutional Neural Networks Based Framework for Skimmed Milk Powder Price Forecasting

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