Recurrent Neural Networks for Financial Time-Series Modelling

Tsang, Gavin; Deng, Jingjing; Xie, Xianghua

doi:10.1109/icpr.2018.8545666

Cited by 19 publications

(9 citation statements)

References 13 publications

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“…Such recurrent architectures by nature are tailored for modelling sequential data with delayed temporal correlations. [18] Many recent works with RNNs have shown good promise in econometric price prediction using either technical indicators [19,20] or social sentiments [21,22].…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Wavelet Denoising and Attention-based RNN- ARIMA Model to Predict Forex Price

Zeng

Khushi

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Every change of trend in the forex market presents a great opportunity as well as a risk for investors. Accurate forecasting of forex prices is a crucial element in any effective hedging or speculation strategy. However, the complex nature of the forex market makes the predicting problem challenging, which has prompted extensive research from various academic disciplines. In this paper, a novel approach that integrates the wavelet denoising, Attention-based Recurrent Neural Network (ARNN), and Autoregressive Integrated Moving Average (ARIMA) are proposed. Wavelet transform removes the noise from the time series to stabilize the data structure. ARNN model captures the robust and non-linear relationships in the sequence and ARIMA can well fit the linear correlation of the sequential information. By hybridization of the three models, the methodology is capable of modelling dynamic systems such as the forex market. Our experiments on USD/JPY five-minute data outperforms the baseline methods. Root-Mean-Squared-Error (RMSE) of the hybrid approach was found to be 1.65 with a directional accuracy of ~76%.

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Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Wavelet Denoising and Attention-based RNN- ARIMA Model to Predict Forex Price

Zeng

Khushi

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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“…Both long-term memory (c[t-1]) and short-term memory (h[t-1]) are processed in a typical LSTM algorithm through the utilization of multiple gates to filter the information showed in the Figure 5. For an unchanged flow of gradients, forget and update gates update the memory cell state [73,74]. Three gates i.e., input gate ig, forgot gate fg and output gate og handle the information flow by writing, deleting, and reading respectively.…”

Section: Long Short-term Memory (Lstm) Recurrent Neuralmentioning

confidence: 99%

Exploring Temporal Dynamics of River Discharge using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

Mehedi¹,

Khosravi²,

Yazdan³

et al. 2022

Preprint

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River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables e.g., precipitation has a significant impact on the temporal distribution of the river discharge in recent days making the discharge forecasting even more complicated for diversified water-related issues e.g., flood prediction and irrigation planning. To predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to 3 days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.09. Higher performance is achieved through the increase in the number of epochs and hyper parameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

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“…They are composed of LSTM cells capable of capturing long-term dependencies in sequences while attenuate gradient vanishing/exploding problem [28]. This capacity is achieved by the use of forget and update gates to modify memory cell state that allow gradients to also flow unchanged [29,30]. The LSTM memory cells are composed by self-loops that encoded temporal information in the cell states, and three regulators gates that operate the flow of information within each cell.…”

Section: Long Short Term Memorymentioning

confidence: 99%

Water Flow Forecasting Based on River Tributaries Using Long Short-Term Memory Ensemble Model

et al. 2021

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Water flow forecasts are an essential information for energy production, management and hydropower control. Advanced actions to optimize electricity production can be taken based on predicted information. This work proposes an ensemble strategy using recurrent neural networks to generate a forecast of water flow at Jirau Hydroelectric Power Plant (HPP), installed on the Madeira River in Brazil. The ensemble strategy consists of combining three long short-term memory (LSTM) networks that model the Madeira River and two of its tributaries: Mamoré and Abunã rivers. The historical data from streamflow of the Madeira river and its tributaries are used to validate the ensemble LSTM model, where each time series of river tributaries are modeled separated by LSTM models and the result used as input for another LSTM model in order to forecast the streamflow of the main river. The experimental results present low errors for training and test sets for individual LSTM networks and ensemble model. In addition, these results were compared with the operational forecasts performed by Jirau HPP. The proposed model showed better accuracy in four of the five scenarios tested, which indicates a promising approach to be explored in water flow forecasting based on river tributaries.

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Recurrent Neural Networks for Financial Time-Series Modelling

Cited by 19 publications

References 13 publications

Wavelet Denoising and Attention-based RNN- ARIMA Model to Predict Forex Price

Wavelet Denoising and Attention-based RNN- ARIMA Model to Predict Forex Price

Exploring Temporal Dynamics of River Discharge using Univariate Long Short-Term Memory (LSTM) Recurrent Neural Network at East Branch of Delaware River

Water Flow Forecasting Based on River Tributaries Using Long Short-Term Memory Ensemble Model

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